id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.02623 | Sample Complexity of Bias Detection with Subsampled Point-to-Subspace
Distances | cs.LG cs.AI math.ST stat.TH | Sample complexity of bias estimation is a lower bound on the runtime of any
bias detection method. Many regulatory frameworks require the bias to be tested
for all subgroups, whose number grows exponentially with the number of
protected attributes. Unless one wishes to run a bias detection with a
doubly-exponential r... |
2502.02624 | Muographic Image Upsampling with Machine Learning for Built
Infrastructure Applications | eess.IV cs.CV | The civil engineering industry faces a critical need for innovative
non-destructive evaluation methods, particularly for ageing critical
infrastructure, such as bridges, where current techniques fall short.
Muography, a non-invasive imaging technique, constructs three-dimensional
density maps by detecting interaction... |
2502.02625 | Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers | cs.LG quant-ph | Parameter shift rules (PSRs) are key techniques for efficient gradient
estimation in variational quantum eigensolvers (VQEs). In this paper, we
propose its Bayesian variant, where Gaussian processes with appropriate kernels
are used to estimate the gradient of the VQE objective. Our Bayesian PSR offers
flexible gradi... |
2502.02628 | e-SimFT: Alignment of Generative Models with Simulation Feedback for
Pareto-Front Design Exploration | cs.LG cs.AI | Deep generative models have recently shown success in solving complex
engineering design problems where models predict solutions that address the
design requirements specified as input. However, there remains a challenge in
aligning such models for effective design exploration. For many design
problems, finding a sol... |
2502.02629 | Graph Structure Learning for Tumor Microenvironment with Cell Type
Annotation from non-spatial scRNA-seq data | q-bio.GN cs.AI cs.LG | The exploration of cellular heterogeneity within the tumor microenvironment
(TME) via single-cell RNA sequencing (scRNA-seq) is essential for understanding
cancer progression and response to therapy. Current scRNA-seq approaches,
however, lack spatial context and rely on incomplete datasets of
ligand-receptor interac... |
2502.02630 | scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based
Prediction for Alzheimer's Disease Diagnosis | q-bio.QM cs.AI cs.LG | Functional MRI (fMRI) and single-cell transcriptomics are pivotal in
Alzheimer's disease (AD) research, each providing unique insights into neural
function and molecular mechanisms. However, integrating these complementary
modalities remains largely unexplored. Here, we introduce scBIT, a novel method
for enhancing A... |
2502.02631 | ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization | cs.LG cs.AI cs.CL cs.CV | The optimal bit-width for achieving the best trade-off between quantized
model size and accuracy has been a subject of ongoing debate. While some
advocate for 4-bit quantization, others propose that 1.58-bit offers superior
results. However, the lack of a cohesive framework for different bits has left
such conclusion... |
2502.02649 | Fully Autonomous AI Agents Should Not be Developed | cs.AI | This paper argues that fully autonomous AI agents should not be developed. In
support of this position, we build from prior scientific literature and current
product marketing to delineate different AI agent levels and detail the ethical
values at play in each, documenting trade-offs in potential benefits and risks.
... |
2502.02657 | SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with
Uncertainty Quantification | cs.RO cs.CV | We present a neural radiance field (NeRF) based large-scale reconstruction
system that fuses lidar and vision data to generate high-quality
reconstructions that are geometrically accurate and capture photorealistic
texture. Our system adopts the state-of-the-art NeRF representation to
additionally incorporate lidar. ... |
2502.02659 | A Training-Free Length Extrapolation Approach for LLMs: Greedy Attention
Logit Interpolation (GALI) | cs.CL cs.AI | Transformer-based Large Language Models (LLMs) struggle to process inputs
exceeding their training context window, with performance degrading due to
positional out-of-distribution (O.O.D.) that disrupt attention computations.
Existing solutions, fine-tuning and training-free methods, are limited by
computational inef... |
2502.02663 | Learning to Double Guess: An Active Perception Approach for Estimating
the Center of Mass of Arbitrary Objects | cs.RO cs.LG | Manipulating arbitrary objects in unstructured environments is a significant
challenge in robotics, primarily due to difficulties in determining an object's
center of mass. This paper introduces U-GRAPH: Uncertainty-Guided Rotational
Active Perception with Haptics, a novel framework to enhance the center of mass
esti... |
2502.02664 | Differentiable Composite Neural Signed Distance Fields for Robot
Navigation in Dynamic Indoor Environments | cs.RO | Neural Signed Distance Fields (SDFs) provide a differentiable environment
representation to readily obtain collision checks and well-defined gradients
for robot navigation tasks. However, updating neural SDFs as the scene evolves
entails re-training, which is tedious, time consuming, and inefficient, making
it unsuit... |
2502.02666 | Deep Reinforcement Learning Enabled Persistent Surveillance with
Energy-Aware UAV-UGV Systems for Disaster Management Applications | cs.RO | Integrating Unmanned Aerial Vehicles (UAVs) with Unmanned Ground Vehicles
(UGVs) provides an effective solution for persistent surveillance in disaster
management. UAVs excel at covering large areas rapidly, but their range is
limited by battery capacity. UGVs, though slower, can carry larger batteries
for extended m... |
2502.02668 | Recovering Imbalanced Clusters via Gradient-Based Projection Pursuit | cs.LG | Projection Pursuit is a classic exploratory technique for finding interesting
projections of a dataset. We propose a method for recovering projections
containing either Imbalanced Clusters or a Bernoulli-Rademacher distribution
using a gradient-based technique to optimize the projection index. As sample
complexity is... |
2502.02669 | Distributed Prescribed-Time Observer for Nonlinear Systems | eess.SY cs.SY | This paper proposes a distributed prescribed-time observer for nonlinear
systems representable in a block-triangular observable canonical form. Using a
weighted average of neighbor estimates exchanged over a strongly connected
digraph, each observer estimates the system state despite limited local sensor
measurements... |
2502.02671 | On Teacher Hacking in Language Model Distillation | cs.LG cs.AI cs.CL stat.ML | Post-training of language models (LMs) increasingly relies on the following
two stages: (i) knowledge distillation, where the LM is trained to imitate a
larger teacher LM, and (ii) reinforcement learning from human feedback (RLHF),
where the LM is aligned by optimizing a reward model. In the second RLHF stage,
a well... |
2502.02672 | Transformers Boost the Performance of Decision Trees on Tabular Data
across Sample Sizes | cs.CL cs.LG | Large language models (LLMs) perform remarkably well on tabular datasets in
zero- and few-shot settings, since they can extract meaning from natural
language column headers that describe features and labels. Similarly, TabPFN, a
recent non-LLM transformer pretrained on numerous tables for in-context
learning, has dem... |
2502.02673 | MedRAX: Medical Reasoning Agent for Chest X-ray | cs.LG cs.AI cs.MA | Chest X-rays (CXRs) play an integral role in driving critical decisions in
disease management and patient care. While recent innovations have led to
specialized models for various CXR interpretation tasks, these solutions often
operate in isolation, limiting their practical utility in clinical practice. We
present Me... |
2502.02676 | Blind Visible Watermark Removal with Morphological Dilation | cs.CV cs.CR cs.LG | Visible watermarks pose significant challenges for image restoration
techniques, especially when the target background is unknown. Toward this end,
we present MorphoMod, a novel method for automated visible watermark removal
that operates in a blind setting -- without requiring target images. Unlike
existing methods,... |
2502.02679 | Networks with Finite VC Dimension: Pro and Contra | stat.ML cs.LG | Approximation and learning of classifiers of large data sets by neural
networks in terms of high-dimensional geometry and statistical learning theory
are investigated. The influence of the VC dimension of sets of input-output
functions of networks on approximation capabilities is compared with its
influence on consis... |
2502.02681 | Building Bridges between Users and Content across Multiple Platforms
during Natural Disasters | cs.SI | Social media is a primary medium for information diffusion during natural
disasters. The social media ecosystem has been used to identify destruction,
analyze opinions and organize aid. While the overall picture and aggregate
trends may be important, a crucial part of the picture is the connections on
these sites. Th... |
2502.02682 | Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning
from Limited Data | cs.LG physics.comp-ph | Neural operators have shown great potential in surrogate modeling. However,
training a well-performing neural operator typically requires a substantial
amount of data, which can pose a major challenge in complex applications. In
such scenarios, detailed physical knowledge can be unavailable or difficult to
obtain, an... |
2502.02683 | Streaming Speaker Change Detection and Gender Classification for
Transducer-Based Multi-Talker Speech Translation | cs.SD cs.AI cs.CL eess.AS | Streaming multi-talker speech translation is a task that involves not only
generating accurate and fluent translations with low latency but also
recognizing when a speaker change occurs and what the speaker's gender is.
Speaker change information can be used to create audio prompts for a zero-shot
text-to-speech syst... |
2502.02684 | Three-dimensional signal processing: a new approach in dynamical
sampling via tensor products | eess.SP cs.IT cs.LG math.IT | The dynamical sampling problem is centered around reconstructing signals that
evolve over time according to a dynamical process, from spatial-temporal
samples that may be noisy. This topic has been thoroughly explored for
one-dimensional signals. Multidimensional signal recovery has also been
studied, but primarily i... |
2502.02685 | A Methodology for Process Design Kit Re-Centering Using TCAD and
Experimental Data for Cryogenic Temperatures | physics.app-ph cond-mat.mes-hall cs.SY eess.SY | In this work, we describe and demonstrate a novel Technology Computer Aided
Design (TCAD) driven methodology that allows measurement data from 'non-ideal'
silicon wafers to be used for re-centering a room temperature-based Process
Design Kit (PDK) to cryogenic temperatures. This comprehensive approach holds
promise f... |
2502.02687 | NDKF: A Neural-Enhanced Distributed Kalman Filter for Nonlinear
Multi-Sensor Estimation | eess.SY cs.SY | We propose a Neural-Enhanced Distributed Kalman Filter (NDKF) for
multi-sensor state estimation in nonlinear systems. Unlike traditional Kalman
filters that rely on explicit, linear models and centralized data fusion, the
NDKF leverages neural networks to learn both the system dynamics and
measurement functions direc... |
2502.02688 | Efficient Implementation of the Global Cardinality Constraint with Costs | cs.AI cs.DS | The success of Constraint Programming relies partly on the global constraints
and implementation of the associated filtering algorithms. Recently, new ideas
emerged to improve these implementations in practice, especially regarding the
all different constraint. In this paper, we consider the cardinality constraint
wi... |
2502.02689 | Multidimensional Swarm Flight Approach For Chasing Unauthorized UAVs
Leveraging Asynchronous Deep Learning | eess.SY cs.SY | This paper introduces a novel unmanned aerial vehicles (UAV) chasing system
designed to track and chase unauthorized UAVs, significantly enhancing their
neutralization effectiveness.
|
2502.02690 | Controllable Video Generation with Provable Disentanglement | cs.CV cs.AI cs.LG | Controllable video generation remains a significant challenge, despite recent
advances in generating high-quality and consistent videos. Most existing
methods for controlling video generation treat the video as a whole, neglecting
intricate fine-grained spatiotemporal relationships, which limits both control
precisio... |
2502.02692 | Intelligent Sensing-to-Action for Robust Autonomy at the Edge:
Opportunities and Challenges | cs.RO cs.CV cs.LG | Autonomous edge computing in robotics, smart cities, and autonomous vehicles
relies on the seamless integration of sensing, processing, and actuation for
real-time decision-making in dynamic environments. At its core is the
sensing-to-action loop, which iteratively aligns sensor inputs with
computational models to dr... |
2502.02696 | How Inclusively do LMs Perceive Social and Moral Norms? | cs.CL | This paper discusses and contains offensive content. Language models (LMs)
are used in decision-making systems and as interactive assistants. However, how
well do these models making judgements align with the diversity of human
values, particularly regarding social and moral norms? In this work, we
investigate how in... |
2502.02700 | Scalable Higher Resolution Polar Sea Ice Classification and Freeboard
Calculation from ICESat-2 ATL03 Data | cs.LG | ICESat-2 (IS2) by NASA is an Earth-observing satellite that measures
high-resolution surface elevation. The IS2's ATL07 and ATL10 sea ice elevation
and freeboard products of 10m-200m segments which aggregated 150 signal photons
from the raw ATL03 (geolocated photon) data. These aggregated products can
potentially ove... |
2502.02701 | Practically Effective Adjustment Variable Selection in Causal Inference | cs.LG cs.AI physics.data-an stat.ME | In the estimation of causal effects, one common method for removing the
influence of confounders is to adjust the variables that satisfy the back-door
criterion. However, it is not always possible to uniquely determine sets of
such variables. Moreover, real-world data is almost always limited, which means
it may be i... |
2502.02703 | Developing multilingual speech synthesis system for Ojibwe, Mi'kmaq, and
Maliseet | cs.CL cs.AI cs.LG cs.SD eess.AS | We present lightweight flow matching multilingual text-to-speech (TTS)
systems for Ojibwe, Mi'kmaq, and Maliseet, three Indigenous languages in North
America. Our results show that training a multilingual TTS model on three
typologically similar languages can improve the performance over monolingual
models, especiall... |
2502.02705 | Rapidly Adapting Policies to the Real World via Simulation-Guided
Fine-Tuning | cs.RO cs.LG | Robot learning requires a considerable amount of high-quality data to realize
the promise of generalization. However, large data sets are costly to collect
in the real world. Physics simulators can cheaply generate vast data sets with
broad coverage over states, actions, and environments. However, physics engines
are... |
2502.02707 | Multiple Instance Learning with Coarse-to-Fine Self-Distillation | cs.CV | Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in
computational pathology often neglects instance-level learning as supervision
is typically provided only at the bag level. In this work, we present PathMIL,
a framework designed to improve MIL through two perspectives: (1) employing
instance-lev... |
2502.02709 | Enforcing Demographic Coherence: A Harms Aware Framework for Reasoning
about Private Data Release | cs.CR cs.DB | The technical literature about data privacy largely consists of two
complementary approaches: formal definitions of conditions sufficient for
privacy preservation and attacks that demonstrate privacy breaches.
Differential privacy is an accepted standard in the former sphere. However,
differential privacy's powerful ... |
2502.02710 | Achievable distributional robustness when the robust risk is only
partially identified | stat.ML cs.LG | In safety-critical applications, machine learning models should generalize
well under worst-case distribution shifts, that is, have a small robust risk.
Invariance-based algorithms can provably take advantage of structural
assumptions on the shifts when the training distributions are heterogeneous
enough to identify ... |
2502.02711 | Tensor Network Structure Search Using Program Synthesis | cs.CE cs.PL | Tensor networks provide a powerful framework for compressing
multi-dimensional data. The optimal tensor network structure for a given data
tensor depends on both the inherent data properties and the specific optimality
criteria, making tensor network structure search a crucial research problem.
Existing solutions typ... |
2502.02715 | An Analysis of LLM Fine-Tuning and Few-Shot Learning for Flaky Test
Detection and Classification | cs.SE cs.AI | Flaky tests exhibit non-deterministic behavior during execution and they may
pass or fail without any changes to the program under test. Detecting and
classifying these flaky tests is crucial for maintaining the robustness of
automated test suites and ensuring the overall reliability and confidence in
the testing. Ho... |
2502.02716 | A Unified Understanding and Evaluation of Steering Methods | cs.LG cs.CL | Steering methods provide a practical approach to controlling large language
models by applying steering vectors to intermediate activations, guiding
outputs toward desired behaviors while avoiding retraining. Despite their
growing importance, the field lacks a unified understanding and consistent
evaluation across ta... |
2502.02717 | Astromer 2 | astro-ph.IM cs.AI cs.LG | Foundational models have emerged as a powerful paradigm in deep learning
field, leveraging their capacity to learn robust representations from
large-scale datasets and effectively to diverse downstream applications such as
classification. In this paper, we present Astromer 2 a foundational model
specifically designed... |
2502.02719 | Beyond Topological Self-Explainable GNNs: A Formal Explainability
Perspective | cs.LG | Self-Explainable Graph Neural Networks (SE-GNNs) are popular
explainable-by-design GNNs, but the properties and the limitations of their
explanations are not well understood. Our first contribution fills this gap by
formalizing the explanations extracted by SE-GNNs, referred to as Trivial
Explanations (TEs), and comp... |
2502.02722 | Cross-Lingual Transfer for Low-Resource Natural Language Processing | cs.CL | Natural Language Processing (NLP) has seen remarkable advances in recent
years, particularly with the emergence of Large Language Models that have
achieved unprecedented performance across many tasks. However, these
developments have mainly benefited a small number of high-resource languages
such as English. The majo... |
2502.02723 | Dobi-SVD: Differentiable SVD for LLM Compression and Some New
Perspectives | cs.LG | We provide a new LLM-compression solution via SVD, unlocking new
possibilities for LLM compression beyond quantization and pruning. We point out
that the optimal use of SVD lies in truncating activations, rather than merely
using activations as an optimization distance. Building on this principle, we
address three cr... |
2502.02725 | The Design of On-Body Robots for Older Adults | cs.RO cs.HC | Wearable technology has significantly improved the quality of life for older
adults, and the emergence of on-body, movable robots presents new opportunities
to further enhance well-being. Yet, the interaction design for these robots
remains under-explored, particularly from the perspective of older adults. We
present... |
2502.02727 | Parameter Tracking in Federated Learning with Adaptive Optimization | cs.LG cs.AI cs.DC | In Federated Learning (FL), model training performance is strongly impacted
by data heterogeneity across clients. Gradient Tracking (GT) has recently
emerged as a solution which mitigates this issue by introducing correction
terms to local model updates. To date, GT has only been considered under
Stochastic Gradient ... |
2502.02732 | Peri-LN: Revisiting Layer Normalization in the Transformer Architecture | cs.LG cs.AI cs.CL | Designing Transformer architectures with the optimal layer normalization (LN)
strategy that ensures large-scale training stability and expedite convergence
has remained elusive, even in this era of large language models (LLMs). To this
end, we present a comprehensive analytical foundation for understanding how
differ... |
2502.02735 | A Modal-Based Approach for System Frequency Response and Frequency Nadir
Prediction | eess.SY cs.SY | This letter introduces a novel approach for predicting system frequency
response and frequency nadir by leveraging modal information. It significantly
differentiates from traditional methods rooted in the average system frequency
model. The proposed methodology targets system modes associated with the slower
dynamics... |
2502.02737 | SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language
Model | cs.CL | While large language models have facilitated breakthroughs in many
applications of artificial intelligence, their inherent largeness makes them
computationally expensive and challenging to deploy in resource-constrained
settings. In this paper, we document the development of SmolLM2, a
state-of-the-art "small" (1.7 b... |
2502.02740 | Vision-Language Model Dialog Games for Self-Improvement | cs.LG cs.AI | The increasing demand for high-quality, diverse training data poses a
significant bottleneck in advancing vision-language models (VLMs). This paper
presents VLM Dialog Games, a novel and scalable self-improvement framework for
VLMs. Our approach leverages self-play between two agents engaged in a
goal-oriented play c... |
2502.02741 | RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical
Image Segmentation with SAM 2 | cs.CV | Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending
SAM to both image and video domains, has shown superior zero-shot performance
compared to its predecessor. Building on SAM's success in medical image
segmentation, SAM 2 presents significant potential for further advancement.
However, simila... |
2502.02743 | LLM Bandit: Cost-Efficient LLM Generation via Preference-Conditioned
Dynamic Routing | cs.LG | The rapid advancement in large language models (LLMs) has brought forth a
diverse range of models with varying capabilities that excel in different tasks
and domains. However, selecting the optimal LLM for user queries often involves
a challenging trade-off between accuracy and cost, a problem exacerbated by the
dive... |
2502.02747 | PatchPilot: A Stable and Cost-Efficient Agentic Patching Framework | cs.RO cs.AI cs.CR | Recent research builds various patching agents that combine large language
models (LLMs) with non-ML tools and achieve promising results on the
state-of-the-art (SOTA) software patching benchmark, SWE-Bench. Based on how to
determine the patching workflows, existing patching agents can be categorized
as agent-based p... |
2502.02748 | ReGNet: Reciprocal Space-Aware Long-Range Modeling and Multi-Property
Prediction for Crystals | cs.LG cond-mat.mtrl-sci | Predicting properties of crystals from their structures is a fundamental yet
challenging task in materials science. Unlike molecules, crystal structures
exhibit infinite periodic arrangements of atoms, requiring methods capable of
capturing both local and global information effectively. However, most current
works fa... |
2502.02753 | MuST: Multi-Head Skill Transformer for Long-Horizon Dexterous
Manipulation with Skill Progress | cs.RO | Robot picking and packing tasks require dexterous manipulation skills, such
as rearranging objects to establish a good grasping pose, or placing and
pushing items to achieve tight packing. These tasks are challenging for robots
due to the complexity and variability of the required actions. To tackle the
difficulty of... |
2502.02756 | Adaptive Voxel-Weighted Loss Using L1 Norms in Deep Neural Networks for
Detection and Segmentation of Prostate Cancer Lesions in PET/CT Images | eess.IV cs.AI cs.CV | This study proposes a new loss function for deep neural networks, L1-weighted
Dice Focal Loss (L1DFL), that leverages L1 norms for adaptive weighting of
voxels based on their classification difficulty, towards automated detection
and segmentation of metastatic prostate cancer lesions in PET/CT scans. We
obtained 380 ... |
2502.02761 | Federated Low-Rank Tensor Estimation for Multimodal Image Reconstruction | cs.LG cs.CV cs.DC | Low-rank tensor estimation offers a powerful approach to addressing
high-dimensional data challenges and can substantially improve solutions to
ill-posed inverse problems, such as image reconstruction under noisy or
undersampled conditions. Meanwhile, tensor decomposition has gained prominence
in federated learning (... |
2502.02763 | Rethinking Vision Transformer for Object Centric Foundation Models | cs.CV | Recent state-of-the-art object segmentation mechanisms, such as the Segment
Anything Model (SAM) and FastSAM, first encode the full image over several
layers and then focus on generating the mask for one particular object or area.
We present an off-grid Fovea-Like Input Patching (FLIP) approach, which selects
image i... |
2502.02764 | LLM-USO: Large Language Model-based Universal Sizing Optimizer | cs.AR cs.LG | The design of analog circuits is a cornerstone of integrated circuit (IC)
development, requiring the optimization of complex, interconnected
sub-structures such as amplifiers, comparators, and buffers. Traditionally,
this process relies heavily on expert human knowledge to refine design
objectives by carefully tuning... |
2502.02766 | Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks | cs.LG cs.IT math.IT | Deep neural networks have achieved state-of-the-art performance across
numerous applications, but their high memory and computational demands present
significant challenges, particularly in resource-constrained environments.
Model compression techniques, such as low-rank approximation, offer a promising
solution by r... |
2502.02768 | Planning with affordances: Integrating learned affordance models and
symbolic planning | cs.AI cs.RO | Intelligent agents working in real-world environments must be able to learn
about the environment and its capabilities which enable them to take actions to
change to the state of the world to complete a complex multi-step task in a
photorealistic environment. Learning about the environment is especially
important to ... |
2502.02770 | Twilight: Adaptive Attention Sparsity with Hierarchical Top-$p$ Pruning | cs.LG cs.CL | Leveraging attention sparsity to accelerate long-context large language
models (LLMs) has been a hot research topic. However, current algorithms such
as sparse attention or key-value (KV) cache compression tend to use a fixed
budget, which presents a significant challenge during deployment because it
fails to account... |
2502.02771 | When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with
Sparse-view CT | physics.med-ph cs.CV cs.LG eess.IV stat.AP | Diffusion models demonstrate state-of-the-art performance on image
generation, and are gaining traction for sparse medical image reconstruction
tasks. However, compared to classical reconstruction algorithms relying on
simple analytical priors, diffusion models have the dangerous property of
producing realistic looki... |
2502.02772 | Cross-Modality Embedding of Force and Language for Natural Human-Robot
Communication | cs.RO cs.AI cs.HC | A method for cross-modality embedding of force profile and words is presented
for synergistic coordination of verbal and haptic communication. When two
people carry a large, heavy object together, they coordinate through verbal
communication about the intended movements and physical forces applied to the
object. This... |
2502.02773 | SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge
using LLMs | cs.RO cs.CV | High-definition maps (HD maps) are detailed and informative maps capturing
lane centerlines and road elements. Although very useful for autonomous
driving, HD maps are costly to build and maintain. Furthermore, access to these
high-quality maps is usually limited to the firms that build them. On the other
hand, stand... |
2502.02774 | Optimal Computational Secret Sharing | cs.IT cs.CR math.IT | In $(t, n)$-threshold secret sharing, a secret $S$ is distributed among $n$
participants such that any subset of size $t$ can recover $S$, while any subset
of size $t-1$ or fewer learns nothing about it. For information-theoretic
secret sharing, it is known that the share size must be at least as large as
the secret,... |
2502.02777 | Symmetry of information for space-bounded online Kolmogorov complexity | cs.CC cs.IT math.IT | The even online Kolmogorov complexity of a string $x = x_1 x_2 \cdots x_{n}$
is the minimal length of a program that for all $i\le n/2$, on input $x_1x_3
\cdots x_{2i-1}$ outputs $x_{2i}$. The odd complexity is defined similarly. The
sum of the odd and even complexities is called the dialogue complexity.
In [Bauwen... |
2502.02779 | 3D Foundation AI Model for Generalizable Disease Detection in Head
Computed Tomography | cs.CV cs.AI | Head computed tomography (CT) imaging is a widely-used imaging modality with
multitudes of medical indications, particularly in assessing pathology of the
brain, skull, and cerebrovascular system. It is commonly the first-line imaging
in neurologic emergencies given its rapidity of image acquisition, safety,
cost, an... |
2502.02780 | Classroom Simulacra: Building Contextual Student Generative Agents in
Online Education for Learning Behavioral Simulation | cs.HC cs.AI cs.LG | Student simulation supports educators to improve teaching by interacting with
virtual students. However, most existing approaches ignore the modulation
effects of course materials because of two challenges: the lack of datasets
with granularly annotated course materials, and the limitation of existing
simulation mode... |
2502.02783 | Runway capacity expansion planning for public airports under demand
uncertainty | eess.SY cs.SY math.OC | Flight delay is a significant issue affecting air travel. The runway system,
frequently falling short of demand, serves as a bottleneck. As demand
increases, runway capacity expansion becomes imperative to mitigate congestion.
However, the decision to expand runway capacity is challenging due to inherent
uncertaintie... |
2502.02785 | OpenSTARLab: Open Approach for Spatio-Temporal Agent Data Analysis in
Soccer | cs.LG | Sports analytics has become both more professional and sophisticated, driven
by the growing availability of detailed performance data. This progress enables
applications such as match outcome prediction, player scouting, and tactical
analysis. In soccer, the effective utilization of event and tracking data is
fundame... |
2502.02786 | When Machine Learning Gets Personal: Understanding Fairness of
Personalized Models | cs.LG | Personalization in machine learning involves tailoring models to individual
users by incorporating personal attributes such as demographic or medical data.
While personalization can improve prediction accuracy, it may also amplify
biases and reduce explainability. This work introduces a unified framework to
evaluate ... |
2502.02787 | SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm
for Large Language Models | cs.CL cs.CR cs.CY cs.LG | The rapid proliferation of large language models (LLMs) has created an urgent
need for reliable methods to detect whether a text is generated by such models.
In this paper, we propose SimMark, a posthoc watermarking algorithm that makes
LLMs' outputs traceable without requiring access to the model's internal
logits, ... |
2502.02788 | Inducing Diversity in Differentiable Search Indexing | cs.IR cs.AI cs.LG | Differentiable Search Indexing (DSI) is a recent paradigm for information
retrieval which uses a transformer-based neural network architecture as the
document index to simplify the retrieval process. A differentiable index has
many advantages enabling modifications, updates or extensions to the index. In
this work, w... |
2502.02789 | Speculative Prefill: Turbocharging TTFT with Lightweight and
Training-Free Token Importance Estimation | cs.CL cs.AI | Improving time-to-first-token (TTFT) is an essentially important objective in
modern large language model (LLM) inference engines. Because optimizing TTFT
directly results in higher maximal QPS and meets the requirements of many
critical applications. However, boosting TTFT is notoriously challenging since
it is pure... |
2502.02790 | Leveraging the true depth of LLMs | cs.LG cs.CL | Large Language Models demonstrate remarkable capabilities at the cost of high
compute requirements. While recent research has shown that intermediate layers
can be removed or have their order shuffled without impacting performance
significantly, these findings have not been employed to reduce the
computational cost o... |
2502.02797 | Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting | cs.LG cs.AI stat.ML | Fine-tuning a pre-trained model on a downstream task often degrades its
original capabilities, a phenomenon known as "catastrophic forgetting". This is
especially an issue when one does not have access to the data and recipe used
to develop the pre-trained model. Under this constraint, most existing methods
for mitig... |
2502.02802 | Consistent Client Simulation for Motivational Interviewing-based
Counseling | cs.CL | Simulating human clients in mental health counseling is crucial for training
and evaluating counselors (both human or simulated) in a scalable manner.
Nevertheless, past research on client simulation did not focus on complex
conversation tasks such as mental health counseling. In these tasks, the
challenge is to ensu... |
2502.02807 | CAMI: A Counselor Agent Supporting Motivational Interviewing through
State Inference and Topic Exploration | cs.CL | Conversational counselor agents have become essential tools for addressing
the rising demand for scalable and accessible mental health support. This paper
introduces CAMI, a novel automated counselor agent grounded in Motivational
Interviewing (MI) -- a client-centered counseling approach designed to address
ambivale... |
2502.02810 | Mol-LLM: Generalist Molecular LLM with Improved Graph Utilization | cs.LG cs.AI physics.chem-ph q-bio.BM | Recent advances in Large Language Models (LLMs) have motivated the
development of general LLMs for molecular tasks. While several studies have
demonstrated that fine-tuned LLMs can achieve impressive benchmark
performances, they are far from genuine generalist molecular LLMs due to a lack
of fundamental understanding... |
2502.02813 | Covert Communications in Active-IOS Aided Uplink NOMA Systems With
Full-Duplex Receiver | cs.IT eess.SP math.IT | In this paper, an active intelligent omni-surface (A-IOS) is deployed to aid
uplink transmissions in a non-orthogonal multiple access (NOMA) system. In
order to shelter the covert signal embedded in the superposition transmissions,
a multi-antenna full-duplex (FD) receiver is utilized at the base-station to
recover s... |
2502.02817 | A Decade of Action Quality Assessment: Largest Systematic Survey of
Trends, Challenges, and Future Directions | cs.AI cs.CV | Action Quality Assessment (AQA) -- the ability to quantify the quality of
human motion, actions, or skill levels and provide feedback -- has far-reaching
implications in areas such as low-cost physiotherapy, sports training, and
workforce development. As such, it has become a critical field in computer
vision & video... |
2502.02818 | Accessible and Portable LLM Inference by Compiling Computational Graphs
into SQL | cs.DB cs.LG | Serving large language models (LLMs) often demands specialized hardware,
dedicated frameworks, and substantial development efforts, which restrict their
accessibility, especially for edge devices and organizations with limited
technical resources. We propose a novel compiler that translates LLM inference
graphs into ... |
2502.02820 | Slowing Learning by Erasing Simple Features | cs.LG | Prior work suggests that neural networks tend to learn low-order moments of
the data distribution first, before moving on to higher-order correlations. In
this work, we derive a novel closed-form concept erasure method, QLEACE, which
surgically removes all quadratically available information about a concept from
a re... |
2502.02821 | AIoT-based smart traffic management system | cs.CV | This paper presents a novel AI-based smart traffic management system
de-signed to optimize traffic flow and reduce congestion in urban environments.
By analysing live footage from existing CCTV cameras, this approach eliminates
the need for additional hardware, thereby minimizing both deployment costs and
ongoing mai... |
2502.02829 | Global Contact-Rich Planning with Sparsity-Rich Semidefinite Relaxations | cs.RO math.OC | We show that contact-rich motion planning is also sparsity-rich when viewed
as polynomial optimization (POP). We can exploit not only the correlative and
term sparsity patterns that are general to all POPs, but also specialized
sparsity patterns from the robot kinematic structure and the separability of
contact modes... |
2502.02830 | Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies | cs.HC cs.LG q-bio.NC | Brain-computer interfaces (BCIs) enable direct communication between the
brain and external devices. This review highlights the core decoding algorithms
that enable multimodal BCIs, including a dissection of the elements, a unified
view of diversified approaches, and a comprehensive analysis of the present
state of t... |
2502.02831 | How the Stroop Effect Arises from Optimal Response Times in Laterally
Connected Self-Organizing Maps | q-bio.NC cs.NE | The Stroop effect refers to cognitive interference in a color-naming task:
When the color and the word do not match, the response is slower and more
likely to be incorrect. The Stroop task is used to assess cognitive
flexibility, selective attention, and executive function. This paper implements
the Stroop task with ... |
2502.02834 | Task-Aware Virtual Training: Enhancing Generalization in
Meta-Reinforcement Learning for Out-of-Distribution Tasks | cs.LG cs.AI | Meta reinforcement learning aims to develop policies that generalize to
unseen tasks sampled from a task distribution. While context-based meta-RL
methods improve task representation using task latents, they often struggle
with out-of-distribution (OOD) tasks. To address this, we propose Task-Aware
Virtual Training (... |
2502.02835 | A Survey of Sample-Efficient Deep Learning for Change Detection in
Remote Sensing: Tasks, Strategies, and Challenges | cs.CV | In the last decade, the rapid development of deep learning (DL) has made it
possible to perform automatic, accurate, and robust Change Detection (CD) on
large volumes of Remote Sensing Images (RSIs). However, despite advances in CD
methods, their practical application in real-world contexts remains limited due
to the... |
2502.02844 | Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement
Learning | cs.LG cs.AI cs.CR cs.MA | Traditional robust methods in multi-agent reinforcement learning (MARL) often
struggle against coordinated adversarial attacks in cooperative scenarios. To
address this limitation, we propose the Wolfpack Adversarial Attack framework,
inspired by wolf hunting strategies, which targets an initial agent and its
assisti... |
2502.02850 | RS-YOLOX: A High Precision Detector for Object Detection in Satellite
Remote Sensing Images | cs.CV | Automatic object detection by satellite remote sensing images is of great
significance for resource exploration and natural disaster assessment. To solve
existing problems in remote sensing image detection, this article proposes an
improved YOLOX model for satellite remote sensing image automatic detection.
This mode... |
2502.02853 | Rethinking Latent Representations in Behavior Cloning: An Information
Bottleneck Approach for Robot Manipulation | cs.RO cs.LG | Behavior Cloning (BC) is a widely adopted visual imitation learning method in
robot manipulation. Current BC approaches often enhance generalization by
leveraging large datasets and incorporating additional visual and textual
modalities to capture more diverse information. However, these methods overlook
whether the ... |
2502.02854 | TD3: Tucker Decomposition Based Dataset Distillation Method for
Sequential Recommendation | cs.IR cs.LG | In the era of data-centric AI, the focus of recommender systems has shifted
from model-centric innovations to data-centric approaches. The success of
modern AI models is built on large-scale datasets, but this also results in
significant training costs. Dataset distillation has emerged as a key solution,
condensing l... |
2502.02856 | PH-VAE: A Polynomial Hierarchical Variational Autoencoder Towards
Disentangled Representation Learning | cs.LG | The variational autoencoder (VAE) is a simple and efficient generative
artificial intelligence method for modeling complex probability distributions
of various types of data, such as images and texts. However, it suffers some
main shortcomings, such as lack of interpretability in the latent variables,
difficulties in... |
2502.02858 | Dexterous Safe Control for Humanoids in Cluttered Environments via
Projected Safe Set Algorithm | cs.RO | It is critical to ensure safety for humanoid robots in real-world
applications without compromising performance. In this paper, we consider the
problem of dexterous safety, featuring limb-level geometry constraints for
avoiding both external and self-collisions in cluttered environments. Compared
to safety with simpl... |
2502.02859 | Gap-Dependent Bounds for Federated $Q$-learning | stat.ML cs.LG | We present the first gap-dependent analysis of regret and communication cost
for on-policy federated $Q$-Learning in tabular episodic finite-horizon Markov
decision processes (MDPs). Existing FRL methods focus on worst-case scenarios,
leading to $\sqrt{T}$-type regret bounds and communication cost bounds with a
$\log... |
2502.02861 | Algorithms with Calibrated Machine Learning Predictions | stat.ML cs.DS cs.LG | The field of algorithms with predictions incorporates machine learning advice
in the design of online algorithms to improve real-world performance. While
this theoretical framework often assumes uniform reliability across all
predictions, modern machine learning models can now provide instance-level
uncertainty estim... |
2502.02862 | Learning Generalizable Features for Tibial Plateau Fracture Segmentation
Using Masked Autoencoder and Limited Annotations | eess.IV cs.AI cs.CV | Accurate automated segmentation of tibial plateau fractures (TPF) from
computed tomography (CT) requires large amounts of annotated data to train deep
learning models, but obtaining such annotations presents unique challenges. The
process demands expert knowledge to identify diverse fracture patterns, assess
severity... |
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