id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.02407 | Avoiding spurious sharpness minimization broadens applicability of SAM | cs.LG cs.CL stat.ML | Curvature regularization techniques like Sharpness Aware Minimization (SAM)
have shown great promise in improving generalization on vision tasks. However,
we find that SAM performs poorly in domains like natural language processing
(NLP), often degrading performance -- even with twice the compute budget. We
investiga... |
2502.02409 | Extending SEEDS to a Supervoxel Algorithm for Medical Image Analysis | cs.CV | In this work, we extend the SEEDS superpixel algorithm from 2D images to 3D
volumes, resulting in 3D SEEDS, a faster, better, and open-source supervoxel
algorithm for medical image analysis. We compare 3D SEEDS with the widely used
supervoxel algorithm SLIC on 13 segmentation tasks across 10 organs. 3D SEEDS
accelera... |
2502.02410 | Privacy Amplification by Structured Subsampling for Deep Differentially
Private Time Series Forecasting | cs.LG cs.CR stat.ML | Many forms of sensitive data, such as web traffic, mobility data, or hospital
occupancy, are inherently sequential. The standard method for training machine
learning models while ensuring privacy for units of sensitive information, such
as individual hospital visits, is differentially private stochastic gradient
desc... |
2502.02414 | Transolver++: An Accurate Neural Solver for PDEs on Million-Scale
Geometries | cs.LG | Although deep models have been widely explored in solving partial
differential equations (PDEs), previous works are primarily limited to data
only with up to tens of thousands of mesh points, far from the million-point
scale required by industrial simulations that involve complex geometries. In
the spirit of advancin... |
2502.02415 | Towards Fast Graph Generation via Autoregressive Noisy Filtration
Modeling | cs.LG | Graph generative models often face a critical trade-off between learning
complex distributions and achieving fast generation speed. We introduce
Autoregressive Noisy Filtration Modeling (ANFM), a novel approach that
addresses both challenges. ANFM leverages filtration, a concept from
topological data analysis, to tra... |
2502.02417 | CVKAN: Complex-Valued Kolmogorov-Arnold Networks | cs.LG | In this work we propose CKAN, a complex-valued KAN, to join the intrinsic
interpretability of KANs and the advantages of Complex-Valued Neural Networks
(CVNNs). We show how to transfer a KAN and the necessary associated mechanisms
into the complex domain. To confirm that CKAN meets expectations we conduct
experiments... |
2502.02421 | Activation-Informed Merging of Large Language Models | cs.CL cs.AI | Model merging, a method that combines the parameters and embeddings of
multiple fine-tuned large language models (LLMs), offers a promising approach
to enhance model performance across various tasks while maintaining
computational efficiency. This paper introduces Activation-Informed Merging
(AIM), a technique that i... |
2502.02424 | Pruning-aware Loss Functions for STOI-Optimized Pruned Recurrent
Autoencoders for the Compression of the Stimulation Patterns of Cochlear
Implants at Zero Delay | cs.SD cs.LG eess.AS | Cochlear implants (CIs) are surgically implanted hearing devices, which allow
to restore a sense of hearing in people suffering from profound hearing loss.
Wireless streaming of audio from external devices to CI signal processors has
become common place. Specialized compression based on the stimulation patterns
of a ... |
2502.02428 | TransformDAS: Mapping {\Phi}-OTDR Signals to Riemannian Manifold for
Robust Classification | cs.LG | Phase-sensitive optical time-domain reflectometry ({\Phi}-OTDR) is a widely
used distributed fiber optic sensing system in engineering. Machine learning
algorithms for {\Phi}-OTDR event classification require high volumes and
quality of datasets; however, high-quality datasets are currently extremely
scarce in the fi... |
2502.02430 | A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals | stat.ML cs.IR cs.LG | Web refresh crawling is the problem of keeping a cache of web pages fresh,
that is, having the most recent copy available when a page is requested, given
a limited bandwidth available to the crawler. Under the assumption that the
change and request events, resp., to each web page follow independent Poisson
processes,... |
2502.02431 | Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated
SGD Variants | cs.LG cs.AI | Recent advancements in deep learning optimization have introduced new
algorithms, such as Schedule-Free optimizers, AdEMAMix, MARS and Lion which
modify traditional momentum mechanisms. In a separate line of work, theoretical
acceleration of stochastic gradient descent (SGD) in noise-dominated regime has
been achieve... |
2502.02433 | A coding theoretic study of homogeneous Markovian predictive games | cs.IT cs.GT math.IT math.PR | This paper explores a predictive game in which a Forecaster announces odds
based on a time-homogeneous Markov kernel, establishing a game-theoretic law of
large numbers for the relative frequencies of occurrences of all finite
strings. A key feature of our proof is a betting strategy built on a universal
coding schem... |
2502.02434 | mPOLICE: Provable Enforcement of Multi-Region Affine Constraints in Deep
Neural Networks | cs.LG | Deep neural networks are increasingly employed in fields such as climate
modeling, robotics, and industrial control, where strict output constraints
must be upheld. Although prior methods like the POLICE algorithm can enforce
affine constraints in a single convex region by adjusting network parameters,
they struggle ... |
2502.02437 | H-MBR: Hypervisor-level Memory Bandwidth Reservation for Mixed
Criticality Systems | cs.DC cs.SY eess.SY | Recent advancements in fields such as automotive and aerospace have driven a
growing demand for robust computational resources. Applications that were once
designed for basic MCUs are now deployed on highly heterogeneous SoC platforms.
While these platforms deliver the necessary computational performance, they
also p... |
2502.02438 | Medical Multimodal Model Stealing Attacks via Adversarial Domain
Alignment | cs.CR cs.AI | Medical multimodal large language models (MLLMs) are becoming an instrumental
part of healthcare systems, assisting medical personnel with decision making
and results analysis. Models for radiology report generation are able to
interpret medical imagery, thus reducing the workload of radiologists. As
medical data is ... |
2502.02439 | System Integrity Protection Schemes in the Nordics -- a comparative
analysis | eess.SY cs.SY | To increase the utilisation rate of the power system and accelerate
electrification while providing a high degree of security and reliability,
System Integrity Protection Schemes (SIPS) are of great importance. SIPS
functions are automatic remedial actions, detecting abnormal conditions or
contingencies in the system... |
2502.02441 | LLMER: Crafting Interactive Extended Reality Worlds with JSON Data
Generated by Large Language Models | cs.MM cs.AI | The integration of Large Language Models (LLMs) like GPT-4 with Extended
Reality (XR) technologies offers the potential to build truly immersive XR
environments that interact with human users through natural language, e.g.,
generating and animating 3D scenes from audio inputs. However, the complexity
of XR environmen... |
2502.02443 | A Null Space Compliance Approach for Maintaining Safety and Tracking
Performance in Human-Robot Interactions | cs.RO cs.SY eess.SY | In recent years, the focus on developing robot manipulators has shifted
towards prioritizing safety in Human-Robot Interaction (HRI). Impedance control
is a typical approach for interaction control in collaboration tasks. However,
such a control approach has two main limitations: 1) the end-effector (EE)'s
limited co... |
2502.02444 | Generative Psycho-Lexical Approach for Constructing Value Systems in
Large Language Models | cs.CL cs.AI | Values are core drivers of individual and collective perception, cognition,
and behavior. Value systems, such as Schwartz's Theory of Basic Human Values,
delineate the hierarchy and interplay among these values, enabling
cross-disciplinary investigations into decision-making and societal dynamics.
Recently, the rise ... |
2502.02446 | Towards graph neural networks for provably solving convex optimization
problems | cs.AI cs.LG cs.NE | Recently, message-passing graph neural networks (MPNNs) have shown potential
for solving combinatorial and continuous optimization problems due to their
ability to capture variable-constraint interactions. While existing approaches
leverage MPNNs to approximate solutions or warm-start traditional solvers, they
often ... |
2502.02448 | Sparse Data Generation Using Diffusion Models | cs.LG | Sparse data is ubiquitous, appearing in numerous domains, from economics and
recommender systems to astronomy and biomedical sciences. However, efficiently
and realistically generating sparse data remains a significant challenge. We
introduce Sparse Data Diffusion (SDD), a novel method for generating sparse
data. SDD... |
2502.02449 | TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video
Understanding in Traffic Scenes | cs.CV | We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for
spatio-temporal video understanding in complex roadside traffic scenarios. The
dataset comprises 1,000 videos, featuring 85,000 multiple-choice QA pairs,
2,300 object captioning, and 5,700 object grounding annotations, encompassing
diverse real... |
2502.02451 | Beyond English: Evaluating Automated Measurement of Moral Foundations in
Non-English Discourse with a Chinese Case Study | cs.CL cs.SI | This study explores computational approaches for measuring moral foundations
(MFs) in non-English corpora. Since most resources are developed primarily for
English, cross-linguistic applications of moral foundation theory remain
limited. Using Chinese as a case study, this paper evaluates the effectiveness
of applyin... |
2502.02452 | Personalization Toolkit: Training Free Personalization of Large Vision
Language Models | cs.CV | Large Vision Language Models (LVLMs) have significant potential to deliver
personalized assistance by adapting to individual users' unique needs and
preferences. Personalization of LVLMs is an emerging area that involves
customizing models to recognize specific object instances and provide tailored
responses. However... |
2502.02454 | IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View
Automated Prompt Learning | cs.CV | Using extensive training data from SA-1B, the Segment Anything Model (SAM)
has demonstrated exceptional generalization and zero-shot capabilities,
attracting widespread attention in areas such as medical image segmentation and
remote sensing image segmentation. However, its performance in the field of
image manipulat... |
2502.02456 | Model Human Learners: Computational Models to Guide Instructional Design | cs.HC cs.AI cs.SC | Instructional designers face an overwhelming array of design choices, making
it challenging to identify the most effective interventions. To address this
issue, I propose the concept of a Model Human Learner, a unified computational
model of learning that can aid designers in evaluating candidate interventions.
This ... |
2502.02457 | Orientation-aware interaction-based deep material network in
polycrystalline materials modeling | cs.CE cs.LG | Multiscale simulations are indispensable for connecting microstructural
features to the macroscopic behavior of polycrystalline materials, but their
high computational demands limit their practicality. Deep material networks
(DMNs) have been proposed as efficient surrogate models, yet they fall short of
capturing tex... |
2502.02458 | SAISA: Towards Multimodal Large Language Models with Both Training and
Inference Efficiency | cs.CL cs.CV | Multimodal Large Language Models (MLLMs) mainly fall into two architectures,
each involving a trade-off between training and inference efficiency: embedding
space alignment (e.g., LLaVA-1.5) is inefficient during inference, while
cross-attention space alignment (e.g., Flamingo) is inefficient in training. In
this pap... |
2502.02463 | Distribution Transformers: Fast Approximate Bayesian Inference With
On-The-Fly Prior Adaptation | stat.ML cs.LG | While Bayesian inference provides a principled framework for reasoning under
uncertainty, its widespread adoption is limited by the intractability of exact
posterior computation, necessitating the use of approximate inference. However,
existing methods are often computationally expensive, or demand costly
retraining ... |
2502.02464 | Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and
Retrieval-Augmented Generation | cs.IR cs.CL | Retrieval, re-ranking, and retrieval-augmented generation (RAG) are critical
components of modern applications in information retrieval, question answering,
or knowledge-based text generation. However, existing solutions are often
fragmented, lacking a unified framework that easily integrates these essential
processe... |
2502.02465 | Towards Consistent and Controllable Image Synthesis for Face Editing | cs.CV | Face editing methods, essential for tasks like virtual avatars, digital human
synthesis and identity preservation, have traditionally been built upon
GAN-based techniques, while recent focus has shifted to diffusion-based models
due to their success in image reconstruction. However, diffusion models still
face challe... |
2502.02468 | High-Fidelity Human Avatars from Laptop Webcams using Edge Compute | cs.CV | Applications of generating photo-realistic human avatars are many, however,
high-fidelity avatar generation traditionally required expensive professional
camera rigs and artistic labor, but recent research has enabled constructing
them automatically from smartphones with RGB and IR sensors. However, these new
methods... |
2502.02470 | Modular Training of Neural Networks aids Interpretability | cs.LG cs.AI | An approach to improve neural network interpretability is via clusterability,
i.e., splitting a model into disjoint clusters that can be studied
independently. We define a measure for clusterability and show that pre-trained
models form highly enmeshed clusters via spectral graph clustering. We thus
train models to b... |
2502.02471 | Mind the Gap: Evaluating Patch Embeddings from General-Purpose and
Histopathology Foundation Models for Cell Segmentation and Classification | cs.CV cs.AI cs.LG q-bio.QM | Recent advancements in foundation models have transformed computer vision,
driving significant performance improvements across diverse domains, including
digital histopathology. However, the advantages of domain-specific
histopathology foundation models over general-purpose models for specialized
tasks such as cell a... |
2502.02472 | SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic
Differential Equations | stat.ML cs.LG | The Latent Stochastic Differential Equation (SDE) is a powerful tool for time
series and sequence modeling. However, training Latent SDEs typically relies on
adjoint sensitivity methods, which depend on simulation and backpropagation
through approximate SDE solutions, which limit scalability. In this work, we
propose... |
2502.02475 | Style transfer as data augmentation: evaluating unpaired image-to-image
translation models in mammography | eess.IV cs.CV physics.med-ph | Several studies indicate that deep learning models can learn to detect breast
cancer from mammograms (X-ray images of the breasts). However, challenges with
overfitting and poor generalisability prevent their routine use in the clinic.
Models trained on data from one patient population may not perform well on
another... |
2502.02479 | Using Random Noise Equivariantly to Boost Graph Neural Networks
Universally | cs.LG | Recent advances in Graph Neural Networks (GNNs) have explored the potential
of random noise as an input feature to enhance expressivity across diverse
tasks. However, naively incorporating noise can degrade performance, while
architectures tailored to exploit noise for specific tasks excel yet lack broad
applicabilit... |
2502.02480 | Stable Port-Hamiltonian Neural Networks | cs.LG | In recent years, nonlinear dynamic system identification using artificial
neural networks has garnered attention due to its manifold potential
applications in virtually all branches of science and engineering. However,
purely data-driven approaches often struggle with extrapolation and may yield
physically implausibl... |
2502.02481 | Multilingual Machine Translation with Open Large Language Models at
Practical Scale: An Empirical Study | cs.CL | Large language models (LLMs) have shown continuously improving multilingual
capabilities, and even small-scale open-source models have demonstrated rapid
performance enhancement. In this paper, we systematically explore the abilities
of open LLMs with less than ten billion parameters to handle multilingual
machine tr... |
2502.02483 | Distributional Diffusion Models with Scoring Rules | cs.LG stat.ML | Diffusion models generate high-quality synthetic data. They operate by
defining a continuous-time forward process which gradually adds Gaussian noise
to data until fully corrupted. The corresponding reverse process progressively
"denoises" a Gaussian sample into a sample from the data distribution. However,
generatin... |
2502.02486 | Catoni Contextual Bandits are Robust to Heavy-tailed Rewards | stat.ML cs.LG | Typical contextual bandit algorithms assume that the rewards at each round
lie in some fixed range $[0, R]$, and their regret scales polynomially with
this reward range $R$. However, many practical scenarios naturally involve
heavy-tailed rewards or rewards where the worst-case range can be substantially
larger than ... |
2502.02487 | Hier-EgoPack: Hierarchical Egocentric Video Understanding with Diverse
Task Perspectives | cs.CV | Our comprehension of video streams depicting human activities is naturally
multifaceted: in just a few moments, we can grasp what is happening, identify
the relevance and interactions of objects in the scene, and forecast what will
happen soon, everything all at once. To endow autonomous systems with such a
holistic ... |
2502.02488 | Do Graph Diffusion Models Accurately Capture and Generate Substructure
Distributions? | cs.LG | Diffusion models have gained popularity in graph generation tasks; however,
the extent of their expressivity concerning the graph distributions they can
learn is not fully understood. Unlike models in other domains, popular
backbones for graph diffusion models, such as Graph Transformers, do not
possess universal exp... |
2502.02489 | A Self-Supervised Framework for Improved Generalisability in Ultrasound
B-mode Image Segmentation | cs.CV cs.AI cs.LG | Ultrasound (US) imaging is clinically invaluable due to its noninvasive and
safe nature. However, interpreting US images is challenging, requires
significant expertise, and time, and is often prone to errors. Deep learning
offers assistive solutions such as segmentation. Supervised methods rely on
large, high-quality... |
2502.02492 | VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion
Generation in Video Models | cs.CV | Despite tremendous recent progress, generative video models still struggle to
capture real-world motion, dynamics, and physics. We show that this limitation
arises from the conventional pixel reconstruction objective, which biases
models toward appearance fidelity at the expense of motion coherence. To
address this, ... |
2502.02493 | EasySpec: Layer-Parallel Speculative Decoding for Efficient Multi-GPU
Utilization | cs.LG | Speculative decoding is an effective and lossless method for Large Language
Model (LLM) inference acceleration. It employs a smaller model to generate a
draft token sequence, which is then verified by the original base model. In
multi-GPU systems, inference latency can be further reduced through tensor
parallelism (T... |
2502.02494 | Analyzing Similarity Metrics for Data Selection for Language Model
Pretraining | cs.LG cs.CL | Similarity between training examples is used to curate pretraining datasets
for language models by many methods -- for diversification and to select
examples similar to high-quality data. However, similarity is typically
measured with off-the-shelf embedding models that are generic or trained for
tasks such as retrie... |
2502.02495 | The Causal-Effect Score in Data Management | cs.DB cs.AI | The Causal Effect (CE) is a numerical measure of causal influence of
variables on observed results. Despite being widely used in many areas, only
preliminary attempts have been made to use CE as an attribution score in data
management, to measure the causal strength of tuples for query answering in
databases. In this... |
2502.02496 | Deep Weight Factorization: Sparse Learning Through the Lens of
Artificial Symmetries | cs.LG stat.ML | Sparse regularization techniques are well-established in machine learning,
yet their application in neural networks remains challenging due to the
non-differentiability of penalties like the $L_1$ norm, which is incompatible
with stochastic gradient descent. A promising alternative is shallow weight
factorization, wh... |
2502.02499 | Learning to generate physical ocean states: Towards hybrid climate
modeling | cs.LG | Ocean General Circulation Models require extensive computational resources to
reach equilibrium states, while deep learning emulators, despite offering fast
predictions, lack the physical interpretability and long-term stability
necessary for climate scientists to understand climate sensitivity (to
greenhouse gas emi... |
2502.02500 | The Skin Game: Revolutionizing Standards for AI Dermatology Model
Comparison | eess.IV cs.CV q-bio.TO | Deep Learning approaches in dermatological image classification have shown
promising results, yet the field faces significant methodological challenges
that impede proper evaluation. This paper presents a dual contribution: first,
a systematic analysis of current methodological practices in skin disease
classificatio... |
2502.02501 | Graph-based Document Structure Analysis | cs.CV | When reading a document, glancing at the spatial layout of a document is an
initial step to understand it roughly. Traditional document layout analysis
(DLA) methods, however, offer only a superficial parsing of documents, focusing
on basic instance detection and often failing to capture the nuanced spatial
and logic... |
2502.02504 | Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian
Trajectory Prediction | cs.CV cs.AI | Pedestrian trajectory prediction aims to forecast future movements based on
historical paths. Spatial-temporal (ST) methods often separately model spatial
interactions among pedestrians and temporal dependencies of individuals. They
overlook the direct impacts of interactions among different pedestrians across
variou... |
2502.02508 | Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM
Reasoning via Autoregressive Search | cs.CL cs.AI | Large language models (LLMs) have demonstrated remarkable reasoning
capabilities across diverse domains. Recent studies have shown that increasing
test-time computation enhances LLMs' reasoning capabilities. This typically
involves extensive sampling at inference time guided by an external LLM
verifier, resulting in ... |
2502.02513 | Generative Modeling on Lie Groups via Euclidean Generalized Score
Matching | cs.LG | We extend Euclidean score-based diffusion processes to generative modeling on
Lie groups. Through the formalism of Generalized Score Matching, our approach
yields a Langevin dynamics which decomposes as a direct sum of Lie algebra
representations, enabling generative processes on Lie groups while operating in
Euclide... |
2502.02514 | Privacy Attacks on Image AutoRegressive Models | cs.CV cs.LG | Image autoregressive (IAR) models have surpassed diffusion models (DMs) in
both image quality (FID: 1.48 vs. 1.58) and generation speed. However, their
privacy risks remain largely unexplored. To address this, we conduct a
comprehensive privacy analysis comparing IARs to DMs. We develop a novel
membership inference a... |
2502.02516 | Adaptive Exploration for Multi-Reward Multi-Policy Evaluation | cs.LG cs.AI stat.ML | We study the policy evaluation problem in an online multi-reward multi-policy
discounted setting, where multiple reward functions must be evaluated
simultaneously for different policies. We adopt an $(\epsilon,\delta)$-PAC
perspective to achieve $\epsilon$-accurate estimates with high confidence
across finite or conv... |
2502.02523 | Brief analysis of DeepSeek R1 and its implications for Generative AI | cs.LG | In late January 2025, DeepSeek released their new reasoning model (DeepSeek
R1); which was developed at a fraction of the cost yet remains competitive with
OpenAI's models, despite the US's GPU export ban. This report discusses the
model, and what its release means for the field of Generative AI more widely.
We brief... |
2502.02525 | Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object
Pose Estimation | cs.CV cs.RO | Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial
for enabling augmented reality and robotic manipulation. Category-level methods
have received extensive research attention due to their potential for
generalization to intra-class unknown objects. However, these methods require
manual collecti... |
2502.02527 | TabPFN Unleashed: A Scalable and Effective Solution to Tabular
Classification Problems | cs.LG | TabPFN has emerged as a promising in-context learning model for tabular data,
capable of directly predicting the labels of test samples given labeled
training examples. It has demonstrated competitive performance, particularly on
small-scale classification tasks. However, despite its effectiveness, TabPFN
still requi... |
2502.02528 | Why human-AI relationships need socioaffective alignment | cs.HC cs.AI | Humans strive to design safe AI systems that align with our goals and remain
under our control. However, as AI capabilities advance, we face a new
challenge: the emergence of deeper, more persistent relationships between
humans and AI systems. We explore how increasingly capable AI agents may
generate the perception ... |
2502.02531 | Deep Linear Network Training Dynamics from Random Initialization: Data,
Width, Depth, and Hyperparameter Transfer | cs.LG cond-mat.dis-nn stat.ML | We theoretically characterize gradient descent dynamics in deep linear
networks trained at large width from random initialization and on large
quantities of random data. Our theory captures the ``wider is better" effect of
mean-field/maximum-update parameterized networks as well as hyperparameter
transfer effects, wh... |
2502.02533 | Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies | cs.LG cs.AI cs.CL cs.MA | Large language models, employed as multiple agents that interact and
collaborate with each other, have excelled at solving complex tasks. The agents
are programmed with prompts that declare their functionality, along with the
topologies that orchestrate interactions across agents. Designing prompts and
topologies for... |
2502.02534 | Adaptive Self-improvement LLM Agentic System for ML Library Development | cs.CL | ML libraries, often written in architecture-specific programming languages
(ASPLs) that target domain-specific architectures, are key to efficient ML
systems. However, writing these high-performance ML libraries is challenging
because it requires expert knowledge of ML algorithms and the ASPL. Large
language models (... |
2502.02537 | Uncertainty Quantification for Collaborative Object Detection Under
Adversarial Attacks | cs.CV cs.LG | Collaborative Object Detection (COD) and collaborative perception can
integrate data or features from various entities, and improve object detection
accuracy compared with individual perception. However, adversarial attacks pose
a potential threat to the deep learning COD models, and introduce high output
uncertainty... |
2502.02538 | Flow Q-Learning | cs.LG cs.AI | We present flow Q-learning (FQL), a simple and performant offline
reinforcement learning (RL) method that leverages an expressive flow-matching
policy to model arbitrarily complex action distributions in data. Training a
flow policy with RL is a tricky problem, due to the iterative nature of the
action generation pro... |
2502.02542 | OverThink: Slowdown Attacks on Reasoning LLMs | cs.LG cs.CR | We increase overhead for applications that rely on reasoning LLMs-we force
models to spend an amplified number of reasoning tokens, i.e., "overthink", to
respond to the user query while providing contextually correct answers. The
adversary performs an OVERTHINK attack by injecting decoy reasoning problems
into the pu... |
2502.02544 | Addressing Label Shift in Distributed Learning via Entropy
Regularization | cs.LG cs.AI | We address the challenge of minimizing true risk in multi-node distributed
learning. These systems are frequently exposed to both inter-node and
intra-node label shifts, which present a critical obstacle to effectively
optimizing model performance while ensuring that data remains confined to each
node. To tackle this... |
2502.02545 | Optimal Spectral Transitions in High-Dimensional Multi-Index Models | cs.LG cond-mat.dis-nn | We consider the problem of how many samples from a Gaussian multi-index model
are required to weakly reconstruct the relevant index subspace. Despite its
increasing popularity as a testbed for investigating the computational
complexity of neural networks, results beyond the single-index setting remain
elusive. In thi... |
2502.02548 | Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D
Segmentation | cs.CV | We tackle open-vocabulary 3D scene understanding by introducing a novel data
generation pipeline and training framework. Our method addresses three critical
requirements for effective training: precise 3D region segmentation,
comprehensive textual descriptions, and sufficient dataset scale. By leveraging
state-of-the... |
2502.02549 | Anytime Incremental $\rho$POMDP Planning in Continuous Spaces | cs.AI cs.LG cs.RO | Partially Observable Markov Decision Processes (POMDPs) provide a robust
framework for decision-making under uncertainty in applications such as
autonomous driving and robotic exploration. Their extension, $\rho$POMDPs,
introduces belief-dependent rewards, enabling explicit reasoning about
uncertainty. Existing onlin... |
2502.02550 | Reachability-Based Contingency Planning against Multi-Modal Predictions
with Branch MPC | eess.SY cs.SY | This paper presents a novel contingency planning framework that integrates
learning-based multi-modal predictions of traffic participants into Branch
Model Predictive Control (MPC). Leveraging reachability analysis, we address
the computational challenges associated with Branch MPC by organizing the
multitude of pred... |
2502.02552 | Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for
Microbiome Analysis | cs.LG q-bio.BM stat.AP stat.CO stat.ME | This paper proposes a hierarchical Bayesian multitask learning model that is
applicable to the general multi-task binary classification learning problem
where the model assumes a shared sparsity structure across different tasks. We
derive a computationally efficient inference algorithm based on variational
inference ... |
2502.02555 | AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late
Dynamic Contrast Enhanced Prostate MRI Synthesis | eess.IV cs.CV | Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a medical
imaging technique that plays a crucial role in the detailed visualization and
identification of tissue perfusion in abnormal lesions and radiological
suggestions for biopsy. However, DCE-MRI involves the administration of a
Gadolinium based (... |
2502.02558 | Particle Trajectory Representation Learning with Masked Point Modeling | hep-ex cs.CV cs.LG | Effective self-supervised learning (SSL) techniques have been key to
unlocking large datasets for representation learning. While many promising
methods have been developed using online corpora and captioned photographs,
their application to scientific domains, where data encodes highly specialized
knowledge, remains ... |
2502.02561 | Decision Theoretic Foundations for Conformal Prediction: Optimal
Uncertainty Quantification for Risk-Averse Agents | cs.LG cs.AI stat.ML | A fundamental question in data-driven decision making is how to quantify the
uncertainty of predictions in ways that can usefully inform downstream action.
This interface between prediction uncertainty and decision-making is especially
important in risk-sensitive domains, such as medicine. In this paper, we
develop d... |
2502.02562 | Learning the RoPEs: Better 2D and 3D Position Encodings with STRING | cs.LG cs.AI cs.CV cs.RO stat.ML | We introduce STRING: Separable Translationally Invariant Position Encodings.
STRING extends Rotary Position Encodings, a recently proposed and widely used
algorithm in large language models, via a unifying theoretical framework.
Importantly, STRING still provides exact translation invariance, including
token coordina... |
2502.02565 | Revisiting Expected Possession Value in Football: Introducing a
Benchmark, U-Net Architecture, and Reward and Risk for Passes | cs.CV cs.LG | This paper introduces the first Expected Possession Value (EPV) benchmark and
a new and improved EPV model for football. Through the introduction of the
OJN-Pass-EPV benchmark, we present a novel method to quantitatively assess the
quality of EPV models by using pairs of game states with given relative EPVs.
Next, we... |
2502.02567 | Fairness in Survival Analysis: A Novel Conditional Mutual Information
Augmentation Approach | cs.LG cs.AI | Survival analysis, a vital tool for predicting the time to event, has been
used in many domains such as healthcare, criminal justice, and finance. Like
classification tasks, survival analysis can exhibit bias against disadvantaged
groups, often due to biases inherent in data or algorithms. Several studies in
both the... |
2502.02573 | Are Language Models Up to Sequential Optimization Problems? From
Evaluation to a Hegelian-Inspired Enhancement | cs.CL cs.AI | Large Language Models (LLMs) have demonstrated impressive capabilities across
numerous fields, presenting an opportunity to revolutionize optimization
problem-solving, a crucial, ubiquitous, and complex domain. This paper explores
the proficiency of LLMs in handling Sequential Optimization Problems (SOPs). We
introdu... |
2502.02577 | A comparison of translation performance between DeepL and Supertext | cs.CL | As strong machine translation (MT) systems are increasingly based on large
language models (LLMs), reliable quality benchmarking requires methods that
capture their ability to leverage extended context. This study compares two
commercial MT systems -- DeepL and Supertext -- by assessing their performance
on unsegment... |
2502.02582 | Open Materials Generation with Stochastic Interpolants | cs.LG cond-mat.mtrl-sci | The discovery of new materials is essential for enabling technological
advancements. Computational approaches for predicting novel materials must
effectively learn the manifold of stable crystal structures within an infinite
design space. We introduce Open Materials Generation (OMG), a unifying
framework for the gene... |
2502.02584 | QLASS: Boosting Language Agent Inference via Q-Guided Stepwise Search | cs.LG cs.AI | Language agents have become a promising solution to complex interactive
tasks. One of the key ingredients to the success of language agents is the
reward model on the trajectory of the agentic workflow, which provides valuable
guidance during training or inference. However, due to the lack of annotations
of intermedi... |
2502.02587 | Spatio-temporal transformer to support automatic sign language
translation | cs.CL | Sign Language Translation (SLT) systems support hearing-impaired people
communication by finding equivalences between signed and spoken languages. This
task is however challenging due to multiple sign variations, complexity in
language and inherent richness of expressions. Computational approaches have
evidenced capa... |
2502.02588 | Calibrated Multi-Preference Optimization for Aligning Diffusion Models | cs.CV | Aligning text-to-image (T2I) diffusion models with preference optimization is
valuable for human-annotated datasets, but the heavy cost of manual data
collection limits scalability. Using reward models offers an alternative,
however, current preference optimization methods fall short in exploiting the
rich informatio... |
2502.02589 | COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for
Fine-Grained Understanding and Generation | cs.CV | This paper introduces the COCONut-PanCap dataset, created to enhance panoptic
segmentation and grounded image captioning. Building upon the COCO dataset with
advanced COCONut panoptic masks, this dataset aims to overcome limitations in
existing image-text datasets that often lack detailed, scene-comprehensive
descrip... |
2502.02590 | Articulate AnyMesh: Open-Vocabulary 3D Articulated Objects Modeling | cs.CV cs.RO | 3D articulated objects modeling has long been a challenging problem, since it
requires to capture both accurate surface geometries and semantically
meaningful and spatially precise structures, parts, and joints. Existing
methods heavily depend on training data from a limited set of handcrafted
articulated object cate... |
2502.02591 | Investigation on the Shooting Method Ability to Solve Different Mooring
Lines Boundary Condition Types | cs.CE | The study of undersea cables and mooring lines statics remains an unavoidable
subject of simulation in offshore field for either steady-state analysis or
dynamic simulation initialization. Whether the study concerns mooring systems
pinned both at seabed and floating platform, cables towed by a moving
underwater syste... |
2502.02592 | A Paradigm Shift to Assembly-like Finite Element Model Updating | cs.CE | In general, there is a mismatch between a finite element model of a structure
and its real behaviour. In aeronautics, this mismatch must be small because
finite element models are a fundamental part of the development of an aircraft
and of increasing importance with the trend to more flexible wings in modern
designs.... |
2502.02593 | Reconstructing 3D Flow from 2D Data with Diffusion Transformer | cs.CE cs.AI physics.flu-dyn | Fluid flow is a widely applied physical problem, crucial in various fields.
Due to the highly nonlinear and chaotic nature of fluids, analyzing
fluid-related problems is exceptionally challenging. Computational fluid
dynamics (CFD) is the best tool for this analysis but involves significant
computational resources, e... |
2502.02594 | Offshore Wind Turbine Tower Design and Optimization: A Review and
AI-Driven Future Directions | cs.CE cs.SY eess.SY | Offshore wind energy leverages the high intensity and consistency of oceanic
winds, playing a key role in the transition to renewable energy. As energy
demands grow, larger turbines are required to optimize power generation and
reduce the Levelized Cost of Energy (LCoE), which represents the average cost
of electrici... |
2502.02602 | A Quasi-Optimal Shape Design Method for Lattice Structure Construction | cs.CE math.OC | Lattice structures, known for their superior mechanical properties, are
widely used in industries such as aerospace, automotive, and biomedical. Their
advantages primarily lie in the interconnected struts at the micro-scale. The
robust construction of these struts is crucial for downstream design and
manufacturing ap... |
2502.02603 | SEAL: Speech Embedding Alignment Learning for Speech Large Language
Model with Retrieval-Augmented Generation | eess.AS cs.CL cs.SD | Embedding-based retrieval models have made significant strides in
retrieval-augmented generation (RAG) techniques for text and multimodal large
language models (LLMs) applications. However, when it comes to speech larage
language models (SLLMs), these methods are limited to a two-stage process,
where automatic speech... |
2502.02605 | Physically Interpretable Representation and Controlled Generation for
Turbulence Data | cs.CE cs.LG physics.comp-ph physics.flu-dyn | Computational Fluid Dynamics (CFD) plays a pivotal role in fluid mechanics,
enabling precise simulations of fluid behavior through partial differential
equations (PDEs). However, traditional CFD methods are resource-intensive,
particularly for high-fidelity simulations of complex flows, which are further
complicated ... |
2502.02607 | MIND: Microstructure INverse Design with Generative Hybrid Neural
Representation | cs.CV cs.GR cs.LG | The inverse design of microstructures plays a pivotal role in optimizing
metamaterials with specific, targeted physical properties. While traditional
forward design methods are constrained by their inability to explore the vast
combinatorial design space, inverse design offers a compelling alternative by
directly gen... |
2502.02610 | Secure & Personalized Music-to-Video Generation via CHARCHA | cs.AI cs.CV cs.HC cs.MM | Music is a deeply personal experience and our aim is to enhance this with a
fully-automated pipeline for personalized music video generation. Our work
allows listeners to not just be consumers but co-creators in the music video
generation process by creating personalized, consistent and context-driven
visuals based o... |
2502.02617 | PolarQuant: Quantizing KV Caches with Polar Transformation | cs.LG cs.AI | Large language models (LLMs) require significant memory to store Key-Value
(KV) embeddings in their KV cache, especially when handling long-range
contexts. Quantization of these KV embeddings is a common technique to reduce
memory consumption. This work introduces PolarQuant, a novel quantization
method employing ran... |
2502.02618 | Deep Learning-Based Facial Expression Recognition for the Elderly: A
Systematic Review | cs.CV cs.AI | The rapid aging of the global population has highlighted the need for
technologies to support elderly, particularly in healthcare and emotional
well-being. Facial expression recognition (FER) systems offer a non-invasive
means of monitoring emotional states, with applications in assisted living,
mental health support... |
2502.02619 | Regret-Optimized Portfolio Enhancement through Deep Reinforcement
Learning and Future Looking Rewards | q-fin.PM cs.LG q-fin.RM | This paper introduces a novel agent-based approach for enhancing existing
portfolio strategies using Proximal Policy Optimization (PPO). Rather than
focusing solely on traditional portfolio construction, our approach aims to
improve an already high-performing strategy through dynamic rebalancing driven
by PPO and Ora... |
2502.02622 | Backcasting the Optimal Decisions in Transport Systems: An Example with
Electric Vehicle Purchase Incentives | math.OC cs.SY eess.SY | This study represents a first attempt to build a backcasting methodology to
identify the optimal policy roadmaps in transport systems. In this methodology,
desired objectives are set by decision makers at a given time horizon, and then
the optimal combinations of policies to achieve these objectives are computed
as a... |
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