id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.00901 | Fundamental limits of learning in sequence multi-index models and deep
attention networks: High-dimensional asymptotics and sharp thresholds | cs.LG cond-mat.dis-nn | In this manuscript, we study the learning of deep attention neural networks,
defined as the composition of multiple self-attention layers, with tied and
low-rank weights. We first establish a mapping of such models to sequence
multi-index models, a generalization of the widely studied multi-index model to
sequential ... |
2502.00902 | Position: More Rigorous Software Engineering Would Improve
Reproducibility in Machine Learning Research | cs.SE cs.LG | Experimental verification and falsification of scholarly work are part of the
scientific method's core. To improve the Machine Learning (ML)-communities'
ability to verify results from prior work, we argue for more robust software
engineering. We estimate the adoption of common engineering best practices by
examining... |
2502.00903 | Embracing Dialectic Intersubjectivity: Coordination of Different
Perspectives in Content Analysis with LLM Persona Simulation | cs.CL cs.AI cs.CY cs.SI | This study attempts to advancing content analysis methodology from
consensus-oriented to coordination-oriented practices, thereby embracing
diverse coding outputs and exploring the dynamics among differential
perspectives. As an exploratory investigation of this approach, we evaluate six
GPT-4o configurations to anal... |
2502.00916 | The Accuracy, Robustness, and Readability of LLM-Generated
Sustainability-Related Word Definitions | cs.CL | A common language with standardized definitions is crucial for effective
climate discussions. However, concerns exist about LLMs misrepresenting climate
terms. We compared 300 official IPCC glossary definitions with those generated
by GPT-4o-mini, Llama3.1 8B, and Mistral 7B, analyzing adherence, robustness,
and read... |
2502.00919 | Attention Sinks and Outlier Features: A 'Catch, Tag, and Release'
Mechanism for Embeddings | cs.CL cs.AI cs.LG | Two prominent features of large language models (LLMs) is the presence of
large-norm (outlier) features and the tendency for tokens to attend very
strongly to a select few tokens. Despite often having no semantic relevance,
these select tokens, called attention sinks, along with the large outlier
features, have prove... |
2502.00920 | Extending the Lattice Boltzmann Method to Non-linear Solid Mechanics | cs.CE cs.NA math.NA | This work outlines a Lattice Boltzmann Method (LBM) for geometrically and
constitutively nonlinear solid mechanics to simulate large deformations under
dynamic loading conditions. The method utilizes the moment chain approach,
where the nonlinear constitutive law is incorporated via a forcing term. Stress
and deforma... |
2502.00921 | Blink of an eye: a simple theory for feature localization in generative
models | cs.LG | Large language models (LLMs) can exhibit undesirable and unexpected behavior
in the blink of an eye. In a recent Anthropic demo, Claude switched from coding
to Googling pictures of Yellowstone, and these sudden shifts in behavior have
also been observed in reasoning patterns and jailbreaks. This phenomenon is not
uni... |
2502.00922 | Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference | cs.LG cs.AR | As they become more capable, large language models (LLMs) have continued to
rapidly increase in size. This has exacerbated the difficulty in running state
of the art LLMs on small, edge devices. Standard techniques advocate solving
this problem through lossy compression techniques such as quantization or
pruning. How... |
2502.00926 | Structured Pneumatic Fingerpads for Actively Tunable Grip Friction | cs.RO | Grip surfaces with tunable friction can actively modify contact conditions,
enabling transitions between higher- and lower-friction states for grasp
adjustment. Friction can be increased to grip securely and then decreased to
gently release (e.g., for handovers) or manipulate in-hand. Recent
friction-tuning surface d... |
2502.00928 | Mathematical Cell Deployment Optimization for Capacity and Coverage of
Ground and UAV Users | cs.IT eess.SP math.IT | We present a general mathematical framework for optimizing cell deployment
and antenna configuration in wireless networks, inspired by quantization
theory. Unlike traditional methods, our framework supports networks with
deterministically located nodes, enabling modeling and optimization under
controlled deployment s... |
2502.00930 | Event-Triggered Newton-Based Extremum Seeking Control | math.OC cs.SY eess.SY | This paper proposes the incorporation of static event-triggered control in
the actuation path of Newton-based extremum seeking and its comparison with the
earlier gradient version. As in the continuous methods, the convergence rate of
the gradient approach depends on the unknown Hessian of the nonlinear map to be
opt... |
2502.00931 | VL-Nav: Real-time Vision-Language Navigation with Spatial Reasoning | cs.RO cs.CV | Vision-language navigation in unknown environments is crucial for mobile
robots. In scenarios such as household assistance and rescue, mobile robots
need to understand a human command, such as "find a person wearing black". We
present a novel vision-language navigation (VL-Nav) system that integrates
efficient spatia... |
2502.00935 | Generalizing Safety Beyond Collision-Avoidance via Latent-Space
Reachability Analysis | cs.RO cs.LG | Hamilton-Jacobi (HJ) reachability is a rigorous mathematical framework that
enables robots to simultaneously detect unsafe states and generate actions that
prevent future failures. While in theory, HJ reachability can synthesize safe
controllers for nonlinear systems and nonconvex constraints, in practice, it
has bee... |
2502.00937 | Towards Efficient Large Multimodal Model Serving | cs.DC cs.AI | Recent advances in generative AI have led to large multi-modal models (LMMs)
capable of simultaneously processing inputs of various modalities such as text,
images, video, and audio. While these models demonstrate impressive
capabilities, efficiently serving them in production environments poses
significant challenge... |
2502.00939 | Fruit Fly Classification (Diptera: Tephritidae) in Images, Applying
Transfer Learning | cs.CV cs.AI | This study develops a transfer learning model for the automated
classification of two species of fruit flies, Anastrepha fraterculus and
Ceratitis capitata, in a controlled laboratory environment. The research
addresses the need to optimize identification and classification, which are
currently performed manually by ... |
2502.00940 | An MDP Model for Censoring in Harvesting Sensors: Optimal and
Approximated Solutions | eess.SY cs.AI cs.SY | In this paper, we propose a novel censoring policy for energy-efficient
transmissions in energy-harvesting sensors. The problem is formulated as an
infinite-horizon Markov Decision Process (MDP). The objective to be optimized
is the expected sum of the importance (utility) of all transmitted messages.
Assuming that s... |
2502.00943 | Universal Abstraction: Harnessing Frontier Models to Structure
Real-World Data at Scale | cs.CL | The vast majority of real-world patient information resides in unstructured
clinical text, and the process of medical abstraction seeks to extract and
normalize structured information from this unstructured input. However,
traditional medical abstraction methods can require significant manual efforts
that can include... |
2502.00944 | Analysis of static and dynamic batching algorithms for graph neural
networks | cs.LG | Graph neural networks (GNN) have shown promising results for several domains
such as materials science, chemistry, and the social sciences. GNN models often
contain millions of parameters, and like other neural network (NN) models, are
often fed only a fraction of the graphs that make up the training dataset in
batch... |
2502.00947 | Minimax Optimality of Classical Scaling Under General Noise Conditions | math.ST cs.LG stat.ML stat.TH | We establish the consistency of classical scaling under a broad class of
noise models, encompassing many commonly studied cases in literature. Our
approach requires only finite fourth moments of the noise, significantly
weakening standard assumptions. We derive convergence rates for classical
scaling and establish ma... |
2502.00952 | Mapping the Spiral of Silence: Surveying Unspoken Opinions in Online
Communities | cs.SI cs.CY cs.HC | We often treat social media as a lens onto society. How might that lens be
distorting the actual popularity of political and social viewpoints? In this
paper, we examine the difference between the viewpoints publicly posted in a
community and the privately surveyed viewpoints of community members,
contributing a meas... |
2502.00954 | Hypo3D: Exploring Hypothetical Reasoning in 3D | cs.CV | The rise of vision-language foundation models marks an advancement in
bridging the gap between human and machine capabilities in 3D scene reasoning.
Existing 3D reasoning benchmarks assume real-time scene accessibility, which is
impractical due to the high cost of frequent scene updates. To this end, we
introduce Hyp... |
2502.00955 | Efficient Multi-Agent System Training with Data Influence-Oriented Tree
Search | cs.CL | Monte Carlo Tree Search (MCTS) based methods provide promising approaches for
generating synthetic data to enhance the self-training of Large Language Model
(LLM) based multi-agent systems (MAS). These methods leverage Q-values to
estimate individual agent contributions. However, relying solely on Q-values to
identif... |
2502.00960 | SAM-guided Pseudo Label Enhancement for Multi-modal 3D Semantic
Segmentation | cs.CV | Multi-modal 3D semantic segmentation is vital for applications such as
autonomous driving and virtual reality (VR). To effectively deploy these models
in real-world scenarios, it is essential to employ cross-domain adaptation
techniques that bridge the gap between training data and real-world data.
Recently, self-tra... |
2502.00963 | PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs | cs.LG | While recent AI-for-math has made strides in pure mathematics, areas of
applied mathematics, particularly PDEs, remain underexplored despite their
significant real-world applications. We present PDE-Controller, a framework
that enables large language models (LLMs) to control systems governed by
partial differential e... |
2502.00964 | ML-Dev-Bench: Comparative Analysis of AI Agents on ML development
workflows | cs.SE cs.AI | In this report, we present ML-Dev-Bench, a benchmark aimed at testing agentic
capabilities on applied Machine Learning development tasks. While existing
benchmarks focus on isolated coding tasks or Kaggle-style competitions,
ML-Dev-Bench tests agents' ability to handle the full complexity of ML
development workflows.... |
2502.00965 | CLIP-UP: A Simple and Efficient Mixture-of-Experts CLIP Training Recipe
with Sparse Upcycling | cs.CV cs.LG | Mixture-of-Experts (MoE) models are crucial for scaling model capacity while
controlling inference costs. While integrating MoE into multimodal models like
CLIP improves performance, training these models is notoriously challenging and
expensive. We propose CLIP-Upcycling (CLIP-UP), an efficient alternative
training ... |
2502.00966 | The Beatbots: A Musician-Informed Multi-Robot Percussion Quartet | cs.RO cs.HC | Artistic creation is often seen as a uniquely human endeavor, yet robots
bring distinct advantages to music-making, such as precise tempo control,
unpredictable rhythmic complexities, and the ability to coordinate intricate
human and robot performances. While many robotic music systems aim to mimic
human musicianship... |
2502.00968 | CoDe: Blockwise Control for Denoising Diffusion Models | cs.CV cs.LG | Aligning diffusion models to downstream tasks often requires finetuning new
models or gradient-based guidance at inference time to enable sampling from the
reward-tilted posterior. In this work, we explore a simple inference-time
gradient-free guidance approach, called controlled denoising (CoDe), that
circumvents th... |
2502.00969 | Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with
Decision Tree Branching | cs.CL | The goal of conversational product search (CPS) is to develop an intelligent,
chat-based shopping assistant that can directly interact with customers to
understand shopping intents, ask clarification questions, and find relevant
products. However, training such assistants is hindered mainly due to the lack
of reliabl... |
2502.00972 | Pushing the Boundaries of State Space Models for Image and Video
Generation | cs.CV cs.LG | While Transformers have become the dominant architecture for visual
generation, linear attention models, such as the state-space models (SSM), are
increasingly recognized for their efficiency in processing long visual
sequences. However, the essential efficiency of these models comes from
formulating a limited recurr... |
2502.00973 | A Wearable Device Dataset for Mental Health Assessment Using Laser
Doppler Flowmetry and Fluorescence Spectroscopy Sensors | cs.LG eess.SP | In this study, we introduce a novel method to predict mental health by
building machine learning models for a non-invasive wearable device equipped
with Laser Doppler Flowmetry (LDF) and Fluorescence Spectroscopy (FS) sensors.
Besides, we present the corresponding dataset to predict mental health, e.g.
depression, an... |
2502.00976 | Learning the Integral Quadratic Constraints on Plant-Model Mismatch | eess.SY cs.SY | While a characterization of plant-model mismatch is necessary for robust
control, the mismatch usually can not be described accurately due to the lack
of knowledge about the plant model or the complexity of nonlinear plants.
Hence, this paper considers this problem in a data-driven way, where the
mismatch is captured... |
2502.00977 | Context-Aware Hierarchical Merging for Long Document Summarization | cs.CL | Hierarchical Merging is a technique commonly used to summarize very long
texts ($>$100K tokens) by breaking down the input into smaller sections,
summarizing those sections individually, and then merging or combining those
summaries into a final coherent summary. Although it helps address the
limitations of large lan... |
2502.00980 | Forecasting VIX using interpretable Kolmogorov-Arnold networks | cs.LG cs.AI cs.CE | This paper presents the use of Kolmogorov-Arnold Networks (KANs) for
forecasting the CBOE Volatility Index (VIX). Unlike traditional MLP-based
neural networks that are often criticized for their black-box nature, KAN
offers an interpretable approach via learnable spline-based activation
functions and symbolification.... |
2502.00983 | CausalCOMRL: Context-Based Offline Meta-Reinforcement Learning with
Causal Representation | cs.LG stat.ML | Context-based offline meta-reinforcement learning (OMRL) methods have
achieved appealing success by leveraging pre-collected offline datasets to
develop task representations that guide policy learning. However, current
context-based OMRL methods often introduce spurious correlations, where task
components are incorre... |
2502.00987 | RandLoRA: Full-rank parameter-efficient fine-tuning of large models | cs.CL cs.AI cs.CV | Low-Rank Adaptation (LoRA) and its variants have shown impressive results in
reducing the number of trainable parameters and memory requirements of large
transformer networks while maintaining fine-tuning performance. However, the
low-rank nature of the weight update inherently limits the representation power
of fine... |
2502.00988 | PlotGen: Multi-Agent LLM-based Scientific Data Visualization via
Multimodal Feedback | cs.CL cs.AI | Scientific data visualization is pivotal for transforming raw data into
comprehensible visual representations, enabling pattern recognition,
forecasting, and the presentation of data-driven insights. However, novice
users often face difficulties due to the complexity of selecting appropriate
tools and mastering visua... |
2502.00989 | ChartCitor: Multi-Agent Framework for Fine-Grained Chart Visual
Attribution | cs.CL cs.AI | Large Language Models (LLMs) can perform chart question-answering tasks but
often generate unverified hallucinated responses. Existing answer attribution
methods struggle to ground responses in source charts due to limited
visual-semantic context, complex visual-text alignment requirements, and
difficulties in boundi... |
2502.00991 | TxnSails: Achieving Serializable Transaction Scheduling with
Self-Adaptive Isolation Level Selection | cs.DB | Achieving the serializable isolation level, regarded as the gold standard for
transaction processing, is costly. Recent studies reveal that adjusting
specific query patterns within a workload can still achieve serializability
even at lower isolation levels. Nevertheless, these studies typically overlook
the trade-off... |
2502.00992 | FCBoost-Net: A Generative Network for Synthesizing Multiple Collocated
Outfits via Fashion Compatibility Boosting | cs.CV cs.MM | Outfit generation is a challenging task in the field of fashion technology,
in which the aim is to create a collocated set of fashion items that complement
a given set of items. Previous studies in this area have been limited to
generating a unique set of fashion items based on a given set of items, without
providing... |
2502.00996 | Self-supervised Analogical Learning using Language Models | cs.CL | Large language models have been shown to suffer from reasoning inconsistency
issues. That is, they fail more in situations unfamiliar to the training data,
even though exact or very similar reasoning paths exist in more common cases
that they can successfully solve. Such observations motivate us to propose
methods th... |
2502.00997 | MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs | cs.CL cs.AI | The recent success of specialized Large Language Models (LLMs) in domains
such as mathematical reasoning and coding has led to growing interest in
methods for merging these expert LLMs into a unified Mixture-of-Experts (MoE)
model, with the goal of enhancing performance in each domain while retaining
effectiveness on... |
2502.01000 | Adapting Foundation Models for Few-Shot Medical Image Segmentation:
Actively and Sequentially | cs.CV | Recent advances in foundation models have brought promising results in
computer vision, including medical image segmentation. Fine-tuning foundation
models on specific low-resource medical tasks has become a standard practice.
However, ensuring reliable and robust model adaptation when the target task has
a large dom... |
2502.01002 | Multi-Resolution SAR and Optical Remote Sensing Image Registration
Methods: A Review, Datasets, and Future Perspectives | cs.CV | Synthetic Aperture Radar (SAR) and optical image registration is essential
for remote sensing data fusion, with applications in military reconnaissance,
environmental monitoring, and disaster management. However, challenges arise
from differences in imaging mechanisms, geometric distortions, and radiometric
propertie... |
2502.01004 | ZeroBP: Learning Position-Aware Correspondence for Zero-shot 6D Pose
Estimation in Bin-Picking | cs.CV | Bin-picking is a practical and challenging robotic manipulation task, where
accurate 6D pose estimation plays a pivotal role. The workpieces in bin-picking
are typically textureless and randomly stacked in a bin, which poses a
significant challenge to 6D pose estimation. Existing solutions are typically
learning-base... |
2502.01009 | Robust Trajectory Generation and Control for Quadrotor Motion Planning
with Field-of-View Control Barrier Certification | cs.RO | Many approaches to multi-robot coordination are susceptible to failure due to
communication loss and uncertainty in estimation. We present a real-time
communication-free distributed algorithm for navigating robots to their desired
goals certified by control barrier functions, that model and control the
onboard sensin... |
2502.01012 | Deep Active Learning based Experimental Design to Uncover Synergistic
Genetic Interactions for Host Targeted Therapeutics | cs.LG q-bio.QM stat.ME | Recent technological advances have introduced new high-throughput methods for
studying host-virus interactions, but testing synergistic interactions between
host gene pairs during infection remains relatively slow and labor intensive.
Identification of multiple gene knockdowns that effectively inhibit viral
replicati... |
2502.01013 | Encrypted Large Model Inference: The Equivariant Encryption Paradigm | cs.CR cs.AI | Large scale deep learning model, such as modern language models and diffusion
architectures, have revolutionized applications ranging from natural language
processing to computer vision. However, their deployment in distributed or
decentralized environments raises significant privacy concerns, as sensitive
data may b... |
2502.01014 | Refining Adaptive Zeroth-Order Optimization at Ease | cs.LG cs.AI | Recently, zeroth-order (ZO) optimization plays an essential role in scenarios
where gradient information is inaccessible or unaffordable, such as black-box
systems and resource-constrained environments. While existing adaptive methods
such as ZO-AdaMM have shown promise, they are fundamentally limited by their
underu... |
2502.01015 | Efficient Model Editing with Task Vector Bases: A Theoretical Framework
and Scalable Approach | cs.LG | Task vectors, which are derived from the difference between pre-trained and
fine-tuned model weights, enable flexible task adaptation and model merging
through arithmetic operations such as addition and negation. However, existing
approaches often rely on heuristics with limited theoretical support, often
leading to ... |
2502.01023 | Vessel segmentation for X-separation | cs.CV q-bio.QM | $\chi$-separation is an advanced quantitative susceptibility mapping (QSM)
method that is designed to generate paramagnetic ($\chi_{para}$) and
diamagnetic ($|\chi_{dia}|$) susceptibility maps, reflecting the distribution
of iron and myelin in the brain. However, vessels have shown artifacts,
interfering with the acc... |
2502.01025 | Knowing When to Stop: Dynamic Context Cutoff for Large Language Models | cs.CL | Large language models (LLMs) process entire input contexts indiscriminately,
which is inefficient in cases where the information required to answer a query
is localized within the context. We present dynamic context cutoff, a
human-inspired method enabling LLMs to self-terminate processing upon acquiring
sufficient t... |
2502.01027 | Adversarial Robustness in Two-Stage Learning-to-Defer: Algorithms and
Guarantees | stat.ML cs.LG | Learning-to-Defer (L2D) facilitates optimal task allocation between AI
systems and decision-makers. Despite its potential, we show that current
two-stage L2D frameworks are highly vulnerable to adversarial attacks, which
can misdirect queries or overwhelm decision agents, significantly degrading
system performance. T... |
2502.01029 | Comprehensive Modeling Approaches for Forecasting Bitcoin Transaction
Fees: A Comparative Study | cs.LG cs.AI | Transaction fee prediction in Bitcoin's ecosystem represents a crucial
challenge affecting both user costs and miner revenue optimization. This study
presents a systematic evaluation of six predictive models for forecasting
Bitcoin transaction fees across a 24-hour horizon (144 blocks): SARIMAX,
Prophet, Time2Vec, Ti... |
2502.01031 | DiffIM: Differentiable Influence Minimization with Surrogate Modeling
and Continuous Relaxation | cs.LG cs.SI | In social networks, people influence each other through social links, which
can be represented as propagation among nodes in graphs. Influence minimization
(IMIN) is the problem of manipulating the structures of an input graph (e.g.,
removing edges) to reduce the propagation among nodes. IMIN can represent
time-criti... |
2502.01032 | Converting MLPs into Polynomials in Closed Form | cs.LG stat.ML | Recent work has shown that purely quadratic functions can replace MLPs in
transformers with no significant loss in performance, while enabling new
methods of interpretability based on linear algebra. In this work, we
theoretically derive closed-form least-squares optimal approximations of
feedforward networks (multil... |
2502.01033 | PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation
Adjustment | cs.CL | In the realm of parameter-efficient fine-tuning (PEFT) methods, while options
like LoRA are available, there is a persistent demand in the industry for a
PEFT approach that excels in both efficiency and performance within the context
of single-backbone multi-tenant applications. This paper introduces a new and
straig... |
2502.01034 | End-to-End Imitation Learning for Optimal Asteroid Proximity Operations | cs.RO cs.LG | Controlling spacecraft near asteroids in deep space comes with many
challenges. The delays involved necessitate heavy usage of limited onboard
computation resources while fuel efficiency remains a priority to support the
long loiter times needed for gathering data. Additionally, the difficulty of
state determination ... |
2502.01035 | UASTHN: Uncertainty-Aware Deep Homography Estimation for UAV
Satellite-Thermal Geo-localization | cs.RO cs.CV | Geo-localization is an essential component of Unmanned Aerial Vehicle (UAV)
navigation systems to ensure precise absolute self-localization in outdoor
environments. To address the challenges of GPS signal interruptions or low
illumination, Thermal Geo-localization (TG) employs aerial thermal imagery to
align with ref... |
2502.01036 | eagle: early approximated gradient based learning rate estimator | cs.LG cs.AI | We propose EAGLE update rule, a novel optimization method that accelerates
loss convergence during the early stages of training by leveraging both current
and previous step parameter and gradient values. The update algorithm estimates
optimal parameters by computing the changes in parameters and gradients between
con... |
2502.01039 | Geoinformatics-Guided Machine Learning for Power Plant Classification | cs.LG | This paper proposes an approach in the area of Knowledge-Guided Machine
Learning (KGML) via a novel integrated framework comprising CNN (Convolutional
Neural Networks) and ViT (Vision Transformers) along with GIS (Geographic
Information Systems) to enhance power plant classification in the context of
energy managemen... |
2502.01041 | Multi-Object Active Search and Tracking by Multiple Agents in Untrusted,
Dynamically Changing Environments | cs.RO | This paper addresses the problem of both actively searching and tracking
multiple unknown dynamic objects in a known environment with multiple
cooperative autonomous agents with partial observability. The tracking of a
target ends when the uncertainty is below a threshold. Current methods
typically assume homogeneous... |
2502.01042 | Internal Activation as the Polar Star for Steering Unsafe LLM Behavior | cs.LG | Large language models (LLMs) have demonstrated exceptional capabilities
across a wide range of tasks but also pose significant risks due to their
potential to generate harmful content. Although existing safety mechanisms can
improve model safety, they often lead to overly cautious behavior and fail to
fully utilize L... |
2502.01043 | Multiphysics Continuous Shape Optimization of the TAP Reactor Components | cs.CE | The Transatomic Power (TAP) reactor has an unusual design for a molten salt
reactor technology, building upon the foundation laid by the Molten Salt
Reactor Experiment (MSRE). This design introduces three key modifications to
enhance efficiency and compactness: a revised fuel salt composition, an
alternative moderato... |
2502.01044 | Nonlinear receding-horizon differential game for drone racing along a
three-dimensional path | eess.SY cs.SY | Drone racing involves high-speed navigation of three-dimensional paths,
posing a substantial challenge in control engineering. This study presents a
game-theoretic control framework, the nonlinear receding-horizon differential
game (NRHDG), designed for competitive drone racing. NRHDG enhances robustness
in adversari... |
2502.01045 | WonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human
Reconstruction | cs.CV cs.GR | In this paper, we present WonderHuman to reconstruct dynamic human avatars
from a monocular video for high-fidelity novel view synthesis. Previous dynamic
human avatar reconstruction methods typically require the input video to have
full coverage of the observed human body. However, in daily practice, one
typically h... |
2502.01046 | Emotional Face-to-Speech | cs.SD cs.CV eess.AS | How much can we infer about an emotional voice solely from an expressive
face? This intriguing question holds great potential for applications such as
virtual character dubbing and aiding individuals with expressive language
disorders. Existing face-to-speech methods offer great promise in capturing
identity characte... |
2502.01048 | Sparks of Explainability: Recent Advancements in Explaining Large Vision
Models | cs.CV cs.AI | This thesis explores advanced approaches to improve explainability in
computer vision by analyzing and modeling the features exploited by deep neural
networks. Initially, it evaluates attribution methods, notably saliency maps,
by introducing a metric based on algorithmic stability and an approach
utilizing Sobol ind... |
2502.01050 | AutoDDG: Automated Dataset Description Generation using Large Language
Models | cs.DB | The proliferation of datasets across open data portals and enterprise data
lakes presents an opportunity for deriving data-driven insights. However,
widely-used dataset search systems rely on keyword searches over dataset
metadata, including descriptions, to facilitate discovery. When these
descriptions are incomplet... |
2502.01051 | Diffusion Model as a Noise-Aware Latent Reward Model for Step-Level
Preference Optimization | cs.CV | Preference optimization for diffusion models aims to align them with human
preferences for images. Previous methods typically leverage Vision-Language
Models (VLMs) as pixel-level reward models to approximate human preferences.
However, when used for step-level preference optimization, these models face
challenges in... |
2502.01053 | Hybrid Firefly Algorithm and Sperm Swarm Optimization Algorithm using
Newton-Raphson Method (HFASSON) and its application in CR-VANET | cs.NE | This paper proposes a new hybrid algorithm, combining FA, SSO, and the N-R
method to accelerate convergence towards global optima, named the Hybrid
Firefly Algorithm and Sperm Swarm Optimization with Newton-Raphson (HFASSON).
The performance of HFASSON is evaluated using 23 benchmark functions from the
CEC 2017 suite... |
2502.01055 | On the Surprising Robustness of Sequential Convex Optimization for
Contact-Implicit Motion Planning | math.OC cs.RO | Contact-implicit motion planning-embedding contact sequencing as implicit
complementarity constraints-holds the promise of leveraging continuous
optimization to discover new contact patterns online. Nevertheless, the
resulting optimization, being an instance of Mathematical Programming with
Complementary Constraints,... |
2502.01056 | Mitigating Hallucinations in Large Vision-Language Models with Internal
Fact-based Contrastive Decoding | cs.CV cs.CL | Large Visual Language Models (LVLMs) integrate visual and linguistic
modalities, exhibiting exceptional performance across various multimodal tasks.
Nevertheless, LVLMs remain vulnerable to the issue of object hallucinations.
Previous efforts to mitigate this issue focus on supervised fine-tuning (SFT)
or incorporati... |
2502.01057 | FetDTIAlign: A Deep Learning Framework for Affine and Deformable
Registration of Fetal Brain dMRI | eess.IV cs.AI | Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure
in utero. Longitudinal and cross-sectional fetal dMRI studies can reveal
crucial neurodevelopmental changes but require precise spatial alignment across
scans and subjects. This is challenging due to low data quality, rapid brain
developmen... |
2502.01059 | Knowledge Synthesis of Photosynthesis Research Using a Large Language
Model | cs.CL cs.AI | The development of biological data analysis tools and large language models
(LLMs) has opened up new possibilities for utilizing AI in plant science
research, with the potential to contribute significantly to knowledge
integration and research gap identification. Nonetheless, current LLMs struggle
to handle complex b... |
2502.01060 | Learning Nonlinearity of Boolean Functions: An Experimentation with
Neural Networks | cs.LG cs.AI cs.CR | This paper investigates the learnability of the nonlinearity property of
Boolean functions using neural networks. We train encoder style deep neural
networks to learn to predict the nonlinearity of Boolean functions from
examples of functions in the form of a truth table and their corresponding
nonlinearity values. W... |
2502.01061 | OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human
Animation Models | cs.CV | End-to-end human animation, such as audio-driven talking human generation,
has undergone notable advancements in the recent few years. However, existing
methods still struggle to scale up as large general video generation models,
limiting their potential in real applications. In this paper, we propose
OmniHuman, a Di... |
2502.01067 | Nearly Tight Bounds for Exploration in Streaming Multi-armed Bandits
with Known Optimality Gap | cs.LG cs.DS | We investigate the sample-memory-pass trade-offs for pure exploration in
multi-pass streaming multi-armed bandits (MABs) with the *a priori* knowledge
of the optimality gap $\Delta_{[2]}$. Here, and throughout, the optimality gap
$\Delta_{[i]}$ is defined as the mean reward gap between the best and the
$i$-th best ar... |
2502.01068 | FastKV: KV Cache Compression for Fast Long-Context Processing with
Token-Selective Propagation | cs.LG cs.CL | While large language models (LLMs) excel at handling long-context sequences,
they require substantial key-value (KV) caches to store contextual information,
which can heavily burden computational efficiency and memory usage. Previous
efforts to compress these KV caches primarily focused on reducing memory
demands but... |
2502.01070 | An Investigation of FP8 Across Accelerators for LLM Inference | cs.LG cs.PF | The introduction of 8-bit floating-point (FP8) computation units in modern AI
accelerators has generated significant interest in FP8-based large language
model (LLM) inference. Unlike 16-bit floating-point formats, FP8 in deep
learning requires a shared scaling factor. Additionally, while E4M3 and E5M2
are well-defin... |
2502.01071 | Scalable, Training-Free Visual Language Robotics: A Modular Multi-Model
Framework for Consumer-Grade GPUs | cs.RO | The integration of language instructions with robotic control, particularly
through Vision Language Action (VLA) models, has shown significant potential.
However, these systems are often hindered by high computational costs, the need
for extensive retraining, and limited scalability, making them less accessible
for w... |
2502.01074 | Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks | cs.LG | Building generalist models has recently demonstrated remarkable capabilities
in diverse scientific domains. Within the realm of molecular learning, several
studies have explored unifying diverse tasks across diverse domains. However,
negative conflicts and interference between molecules and knowledge from
different d... |
2502.01076 | qNBO: quasi-Newton Meets Bilevel Optimization | cs.LG math.OC | Bilevel optimization, addressing challenges in hierarchical learning tasks,
has gained significant interest in machine learning. The practical
implementation of the gradient descent method to bilevel optimization
encounters computational hurdles, notably the computation of the exact
lower-level solution and the inver... |
2502.01078 | Parallel Coding for Orthogonal Delay-Doppler Division Multiplexing | cs.IT eess.SP math.IT | This paper proposes a novel parallel coding transmission strategy and an
iterative detection and decoding receiver signal processing technique for
orthogonal delay-Doppler division multiplexing (ODDM) modulation. Specifically,
the proposed approach employs a parallel channel encoding (PCE) scheme that
consists of mul... |
2502.01080 | BC-GAN: A Generative Adversarial Network for Synthesizing a Batch of
Collocated Clothing | cs.CV cs.MM | Collocated clothing synthesis using generative networks has become an
emerging topic in the field of fashion intelligence, as it has significant
potential economic value to increase revenue in the fashion industry. In
previous studies, several works have attempted to synthesize
visually-collocated clothing based on a... |
2502.01081 | The Jumping Reasoning Curve? Tracking the Evolution of Reasoning
Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles | cs.CV cs.AI cs.CL | The releases of OpenAI's o1 and o3 mark a significant paradigm shift in Large
Language Models towards advanced reasoning capabilities. Notably, o3
outperformed humans in novel problem-solving and skill acquisition on the
Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI).
However, this ben... |
2502.01083 | Tool Unlearning for Tool-Augmented LLMs | cs.LG cs.AI cs.CL | Tool-augmented large language models (LLMs) are often trained on datasets of
query-response pairs, which embed the ability to use tools or APIs directly
into the parametric knowledge of LLMs. Tool-augmented LLMs need the ability to
forget learned tools due to security vulnerabilities, privacy regulations, or
tool dep... |
2502.01084 | Continuous Autoregressive Modeling with Stochastic Monotonic Alignment
for Speech Synthesis | cs.LG cs.SD eess.AS | We propose a novel autoregressive modeling approach for speech synthesis,
combining a variational autoencoder (VAE) with a multi-modal latent space and
an autoregressive model that uses Gaussian Mixture Models (GMM) as the
conditional probability distribution. Unlike previous methods that rely on
residual vector quan... |
2502.01085 | Federated Linear Dueling Bandits | cs.LG | Contextual linear dueling bandits have recently garnered significant
attention due to their widespread applications in important domains such as
recommender systems and large language models. Classical dueling bandit
algorithms are typically only applicable to a single agent. However, many
applications of dueling ban... |
2502.01089 | Advanced Architectures Integrated with Agentic AI for Next-Generation
Wireless Networks | cs.NI cs.AI | This paper investigates a range of cutting-edge technologies and
architectural innovations aimed at simplifying network operations, reducing
operational expenditure (OpEx), and enabling the deployment of new service
models. The focus is on (i) Proposing novel, more efficient 6G architectures,
with both Control and Us... |
2502.01090 | Classic4Children: Adapting Chinese Literary Classics for Children with
Large Language Model | cs.CL cs.AI | Chinese literary classics hold significant cultural and educational value,
offering deep insights into morality, history, and human nature. These works
often include classical Chinese and complex narratives, making them difficult
for children to read. To bridge this gap, we introduce a child-friendly
literary adaptat... |
2502.01091 | Enhancing Aspect-based Sentiment Analysis with ParsBERT in Persian
Language | cs.CL cs.AI | In the era of pervasive internet use and the dominance of social networks,
researchers face significant challenges in Persian text mining including the
scarcity of adequate datasets in Persian and the inefficiency of existing
language models. This paper specifically tackles these challenges, aiming to
amplify the eff... |
2502.01092 | Enhancing Feature Tracking Reliability for Visual Navigation using
Real-Time Safety Filter | cs.RO cs.CV cs.SY eess.SY | Vision sensors are extensively used for localizing a robot's pose,
particularly in environments where global localization tools such as GPS or
motion capture systems are unavailable. In many visual navigation systems,
localization is achieved by detecting and tracking visual features or
landmarks, which provide infor... |
2502.01094 | Model Order Reduction from Data with Certification | eess.SY cs.SY | Model order reduction (MOR) involves offering low-dimensional models that
effectively approximate the behavior of complex high-order systems. Due to
potential model complexities and computational costs, designing controllers for
high-dimensional systems with complex behaviors can be challenging, rendering
MOR a pract... |
2502.01098 | SatFlow: Generative model based framework for producing High Resolution
Gap Free Remote Sensing Imagery | cs.CV cs.LG | Frequent, high-resolution remote sensing imagery is crucial for agricultural
and environmental monitoring. Satellites from the Landsat collection offer
detailed imagery at 30m resolution but with lower temporal frequency, whereas
missions like MODIS and VIIRS provide daily coverage at coarser resolutions.
Clouds and ... |
2502.01100 | ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning | cs.AI cs.CL cs.LG | We investigate the logical reasoning capabilities of large language models
(LLMs) and their scalability in complex non-monotonic reasoning. To this end,
we introduce ZebraLogic, a comprehensive evaluation framework for assessing LLM
reasoning performance on logic grid puzzles derived from constraint
satisfaction prob... |
2502.01101 | VidSketch: Hand-drawn Sketch-Driven Video Generation with Diffusion
Control | cs.CV cs.AI | With the advancement of generative artificial intelligence, previous studies
have achieved the task of generating aesthetic images from hand-drawn sketches,
fulfilling the public's needs for drawing. However, these methods are limited
to static images and lack the ability to control video animation generation
using h... |
2502.01102 | Towards Robust and Generalizable Lensless Imaging with Modular Learned
Reconstruction | eess.IV cs.CV | Lensless cameras disregard the conventional design that imaging should mimic
the human eye. This is done by replacing the lens with a thin mask, and moving
image formation to the digital post-processing. State-of-the-art lensless
imaging techniques use learned approaches that combine physical modeling and
neural netw... |
2502.01105 | LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion
Transformer | cs.CV | Generating cognitive-aligned layered SVGs remains challenging due to existing
methods' tendencies toward either oversimplified single-layer outputs or
optimization-induced shape redundancies. We propose LayerTracer, a diffusion
transformer based framework that bridges this gap by learning designers'
layered SVG creat... |
2502.01106 | Can We Validate Counterfactual Estimations in the Presence of General
Network Interference? | cs.LG econ.EM stat.ME stat.ML | In experimental settings with network interference, a unit's treatment can
influence outcomes of other units, challenging both causal effect estimation
and its validation. Classic validation approaches fail as outcomes are only
observable under one treatment scenario and exhibit complex correlation
patterns due to in... |
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