id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.06719 | Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient
Descent | stat.ML cs.LG math.OC math.PR math.ST stat.TH | In this paper, we establish non-asymptotic convergence rates in the central
limit theorem for Polyak-Ruppert-averaged iterates of stochastic gradient
descent (SGD). Our analysis builds on the result of the Gaussian approximation
for nonlinear statistics of independent random variables of Shao and Zhang
(2022). Using ... |
2502.06722 | HetSwarm: Cooperative Navigation of Heterogeneous Swarm in Dynamic and
Dense Environments through Impedance-based Guidance | cs.RO | With the growing demand for efficient logistics and warehouse management,
unmanned aerial vehicles (UAVs) are emerging as a valuable complement to
automated guided vehicles (AGVs). UAVs enhance efficiency by navigating dense
environments and operating at varying altitudes. However, their limited flight
time, battery ... |
2502.06725 | AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in
Dynamic Environments by Leveraging Object Detection | cs.RO | Autonomous drone navigation in dynamic environments remains a critical
challenge, especially when dealing with unpredictable scenarios including
fast-moving objects with rapidly changing goal positions. While traditional
planners and classical optimisation methods have been extensively used to
address this dynamic pr... |
2502.06726 | Rough Stochastic Pontryagin Maximum Principle and an Indirect Shooting
Method | math.OC cs.RO cs.SY eess.SY math.PR | We derive first-order Pontryagin optimality conditions for stochastic optimal
control with deterministic controls for systems modeled by rough differential
equations (RDE) driven by Gaussian rough paths. This Pontryagin Maximum
Principle (PMP) applies to systems following stochastic differential equations
(SDE) drive... |
2502.06727 | Application of Artificial Intelligence (AI) in Civil Engineering | cs.AI | Hard computing generally deals with precise data, which provides ideal
solutions to problems. However, in the civil engineering field, amongst other
disciplines, that is not always the case as real-world systems are continuously
changing. Here lies the need to explore soft computing methods and artificial
intelligenc... |
2502.06728 | FlexDeMo: Decoupled Momentum Optimization for Fully and Hybrid Sharded
Training | cs.LG cs.AI | Training large neural network models requires extensive computational
resources, often distributed across several nodes and accelerators. Recent
findings suggest that it may be sufficient to only exchange the fast moving
components of the gradients, while accumulating momentum locally (Decoupled
Momentum, or DeMo). H... |
2502.06733 | Dynamic Loss-Based Sample Reweighting for Improved Large Language Model
Pretraining | cs.LG cs.AI | Pretraining large language models (LLMs) on vast and heterogeneous datasets
is crucial for achieving state-of-the-art performance across diverse downstream
tasks. However, current training paradigms treat all samples equally,
overlooking the importance or relevance of individual samples throughout the
training proces... |
2502.06734 | Se\~norita-2M: A High-Quality Instruction-based Dataset for General
Video Editing by Video Specialists | cs.CV | Recent advancements in video generation have spurred the development of video
editing techniques, which can be divided into inversion-based and end-to-end
methods. However, current video editing methods still suffer from several
challenges. Inversion-based methods, though training-free and flexible, are
time-consumin... |
2502.06735 | Enhancing Pneumonia Diagnosis and Severity Assessment through Deep
Learning: A Comprehensive Approach Integrating CNN Classification and
Infection Segmentation | cs.CV | Lung disease poses a substantial global health challenge, with pneumonia
being a prevalent concern. This research focuses on leveraging deep learning
techniques to detect and assess pneumonia, addressing two interconnected
objectives. Initially, Convolutional Neural Network (CNN) models are introduced
for pneumonia c... |
2502.06736 | Low-power Spike-based Wearable Analytics on RRAM Crossbars | cs.ET cs.AI cs.AR | This work introduces a spike-based wearable analytics system utilizing
Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based
on RRAM crossbars, which are known for their compactness and energy-efficiency.
Given the hardware constraints and noise characteristics of the underlying RRAM
crossbar... |
2502.06737 | VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data | cs.LG | Process Reward Models (PRMs) have proven effective at enhancing mathematical
reasoning for Large Language Models (LLMs) by leveraging increased
inference-time computation. However, they are predominantly trained on
mathematical data and their generalizability to non-mathematical domains has
not been rigorously studie... |
2502.06738 | Resurrecting saturated LLM benchmarks with adversarial encoding | cs.LG | Recent work showed that small changes in benchmark questions can reduce LLMs'
reasoning and recall. We explore two such changes: pairing questions and adding
more answer options, on three benchmarks: WMDP-bio, GPQA, and MMLU variants. We
find that for more capable models, these predictably reduce performance,
essenti... |
2502.06739 | A note on the physical interpretation of neural PDE's | cs.LG cond-mat.dis-nn physics.comp-ph | We highlight a formal and substantial analogy between Machine Learning (ML)
algorithms and discrete dynamical systems (DDS) in relaxation form. The analogy
offers a transparent interpretation of the weights in terms of physical
information-propagation processes and identifies the model function of the
forward ML step... |
2502.06741 | ViSIR: Vision Transformer Single Image Reconstruction Method for Earth
System Models | cs.CV | Purpose: Earth system models (ESMs) integrate the interactions of the
atmosphere, ocean, land, ice, and biosphere to estimate the state of regional
and global climate under a wide variety of conditions. The ESMs are highly
complex, and thus, deep neural network architectures are used to model the
complexity and store... |
2502.06742 | Gradient Multi-Normalization for Stateless and Scalable LLM Training | cs.LG cs.AI | Training large language models (LLMs) typically relies on adaptive optimizers
like Adam (Kingma & Ba, 2015) which store additional state information to
accelerate convergence but incur significant memory overhead. Recent efforts,
such as SWAN (Ma et al., 2024) address this by eliminating the need for
optimizer states... |
2502.06747 | Wandering around: A bioinspired approach to visual attention through
object motion sensitivity | cs.CV | Active vision enables dynamic visual perception, offering an alternative to
static feedforward architectures in computer vision, which rely on large
datasets and high computational resources. Biological selective attention
mechanisms allow agents to focus on salient Regions of Interest (ROIs),
reducing computational ... |
2502.06748 | Institutional Preferences in the Laboratory | cs.SI cs.GT | Getting a group to adopt cooperative norms is an enduring challenge. But in
real-world settings, individuals don't just passively accept static
environments, they act both within and upon the social systems that structure
their interactions. Should we expect the dynamism of player-driven changes to
the "rules of the ... |
2502.06749 | Incentivizing Desirable Effort Profiles in Strategic Classification: The
Role of Causality and Uncertainty | cs.GT cs.CY cs.LG | We study strategic classification in binary decision-making settings where
agents can modify their features in order to improve their classification
outcomes. Importantly, our work considers the causal structure across different
features, acknowledging that effort in a given feature may affect other
features. The mai... |
2502.06750 | Accelerating Data Processing and Benchmarking of AI Models for Pathology | cs.CV | Advances in foundation modeling have reshaped computational pathology.
However, the increasing number of available models and lack of standardized
benchmarks make it increasingly complex to assess their strengths, limitations,
and potential for further development. To address these challenges, we
introduce a new suit... |
2502.06751 | What makes a good feedforward computational graph? | cs.LG cs.AI cs.SI stat.ML | As implied by the plethora of literature on graph rewiring, the choice of
computational graph employed by a neural network can make a significant impact
on its downstream performance. Certain effects related to the computational
graph, such as under-reaching and over-squashing, may even render the model
incapable of ... |
2502.06753 | Case for a unified surrogate modelling framework in the age of AI | stat.CO cs.LG | Surrogate models are widely used in natural sciences, engineering, and
machine learning to approximate complex systems and reduce computational costs.
However, the current landscape lacks standardisation across key stages of the
pipeline, including data collection, sampling design, model class selection,
evaluation m... |
2502.06755 | Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision
Models | cs.CV | To truly understand vision models, we must not only interpret their learned
features but also validate these interpretations through controlled
experiments. Current approaches either provide interpretable features without
the ability to test their causal influence, or enable model editing without
interpretable contro... |
2502.06756 | SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement | cs.CV | In this paper, we explore a principal way to enhance the quality of widely
pre-existing coarse masks, enabling them to serve as reliable training data for
segmentation models to reduce the annotation cost. In contrast to prior
refinement techniques that are tailored to specific models or tasks in a
close-world manner... |
2502.06759 | Rationalization Models for Text-to-SQL | cs.CL cs.AI cs.DB | We introduce a framework for generating Chain-of-Thought (CoT) rationales to
enhance text-to-SQL model fine-tuning. These rationales consist of intermediate
SQL statements and explanations, serving as incremental steps toward
constructing the final SQL query. The process begins with manually annotating a
small set of... |
2502.06760 | Infinite-Horizon Value Function Approximation for Model Predictive
Control | cs.RO | Model Predictive Control has emerged as a popular tool for robots to generate
complex motions. However, the real-time requirement has limited the use of hard
constraints and large preview horizons, which are necessary to ensure safety
and stability. In practice, practitioners have to carefully design cost
functions t... |
2502.06761 | When, Where and Why to Average Weights? | cs.LG | Averaging checkpoints along the training trajectory is a simple yet powerful
approach to improve the generalization performance of Machine Learning models
and reduce training time. Motivated by these potential gains, and in an effort
to fairly and thoroughly benchmark this technique, we present an extensive
evaluatio... |
2502.06764 | History-Guided Video Diffusion | cs.LG cs.CV | Classifier-free guidance (CFG) is a key technique for improving conditional
generation in diffusion models, enabling more accurate control while enhancing
sample quality. It is natural to extend this technique to video diffusion,
which generates video conditioned on a variable number of context frames,
collectively r... |
2502.06765 | Are all models wrong? Fundamental limits in distribution-free empirical
model falsification | math.ST cs.LG stat.ML stat.TH | In statistics and machine learning, when we train a fitted model on available
data, we typically want to ensure that we are searching within a model class
that contains at least one accurate model -- that is, we would like to ensure
an upper bound on the model class risk (the lowest possible risk that can be
attained... |
2502.06766 | Exploiting Sparsity for Long Context Inference: Million Token Contexts
on Commodity GPUs | cs.CL | There is growing demand for performing inference with hundreds of thousands
of input tokens on trained transformer models. Inference at this extreme scale
demands significant computational resources, hindering the application of
transformers at long contexts on commodity (i.e not data center scale)
hardware. To addre... |
2502.06768 | Train for the Worst, Plan for the Best: Understanding Token Ordering in
Masked Diffusions | cs.LG | In recent years, masked diffusion models (MDMs) have emerged as a promising
alternative approach for generative modeling over discrete domains. Compared to
autoregressive models (ARMs), MDMs trade off complexity at training time with
flexibility at inference time. At training time, they must learn to solve an
exponen... |
2502.06770 | Parameter-Dependent Control Lyapunov Functions for Stabilizing Nonlinear
Parameter-Varying Systems | math.OC cs.SY eess.SY | This paper introduces the concept of parameter-dependent (PD) control
Lyapunov functions (CLFs) for gainscheduled stabilization of nonlinear
parameter-varying (NPV) systems under input constraints. It shows that given a
PD-CLF, a locally Lipschitz control law can be constructed by solving a robust
quadratic program. ... |
2502.06771 | Unsupervised Particle Tracking with Neuromorphic Computing | hep-ex cs.ET cs.LG cs.NE | We study the application of a neural network architecture for identifying
charged particle trajectories via unsupervised learning of delays and synaptic
weights using a spike-time-dependent plasticity rule. In the considered model,
the neurons receive time-encoded information on the position of particle hits
in a tra... |
2502.06772 | ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates | cs.CL cs.AI cs.LG | We present that hierarchical LLM reasoning via scaling thought templates can
effectively optimize the reasoning search space and outperform the mathematical
reasoning capabilities of powerful LLMs like OpenAI o1-preview and DeepSeek V3.
We train our ReasonFlux-32B model with only 8 GPUs and introduces three
innovatio... |
2502.06773 | On the Emergence of Thinking in LLMs I: Searching for the Right
Intuition | cs.AI cs.CL cs.LG | Recent AI advancements, such as OpenAI's new models, are transforming LLMs
into LRMs (Large Reasoning Models) that perform reasoning during inference,
taking extra time and compute for higher-quality outputs. We aim to uncover the
algorithmic framework for training LRMs. Methods like self-consistency, PRM,
and AlphaZ... |
2502.06774 | ENFORCE: Exact Nonlinear Constrained Learning with Adaptive-depth Neural
Projection | cs.LG | Ensuring neural networks adhere to domain-specific constraints is crucial for
addressing safety and ethical concerns while also enhancing prediction
accuracy. Despite the nonlinear nature of most real-world tasks, existing
methods are predominantly limited to affine or convex constraints. We introduce
ENFORCE, a neur... |
2502.06775 | Enhancing Performance of Explainable AI Models with Constrained Concept
Refinement | cs.LG | The trade-off between accuracy and interpretability has long been a challenge
in machine learning (ML). This tension is particularly significant for emerging
interpretable-by-design methods, which aim to redesign ML algorithms for
trustworthy interpretability but often sacrifice accuracy in the process. In
this paper... |
2502.06776 | Towards Internet-Scale Training For Agents | cs.LG cs.AI | The predominant approach for training web navigation agents gathers human
demonstrations for a set of popular websites and hand-written tasks, but it is
becoming clear that human data are an inefficient resource. We develop a
pipeline to facilitate Internet-scale training for agents without laborious
human annotation... |
2502.06777 | Learning an Optimal Assortment Policy under Observational Data | stat.ML cs.LG math.OC math.ST stat.TH | We study the fundamental problem of offline assortment optimization under the
Multinomial Logit (MNL) model, where sellers must determine the optimal subset
of the products to offer based solely on historical customer choice data. While
most existing approaches to learning-based assortment optimization focus on the
o... |
2502.06779 | KARST: Multi-Kernel Kronecker Adaptation with Re-Scaling Transmission
for Visual Classification | cs.CV cs.AI | Fine-tuning pre-trained vision models for specific tasks is a common practice
in computer vision. However, this process becomes more expensive as models grow
larger. Recently, parameter-efficient fine-tuning (PEFT) methods have emerged
as a popular solution to improve training efficiency and reduce storage needs
by t... |
2502.06781 | Exploring the Limit of Outcome Reward for Learning Mathematical
Reasoning | cs.CL cs.LG | Reasoning abilities, especially those for solving complex math problems, are
crucial components of general intelligence. Recent advances by proprietary
companies, such as o-series models of OpenAI, have made remarkable progress on
reasoning tasks. However, the complete technical details remain unrevealed, and
the tec... |
2502.06782 | Lumina-Video: Efficient and Flexible Video Generation with Multi-scale
Next-DiT | cs.CV | Recent advancements have established Diffusion Transformers (DiTs) as a
dominant framework in generative modeling. Building on this success,
Lumina-Next achieves exceptional performance in the generation of
photorealistic images with Next-DiT. However, its potential for video
generation remains largely untapped, with... |
2502.06784 | RelGNN: Composite Message Passing for Relational Deep Learning | cs.LG cs.AI cs.DB | Predictive tasks on relational databases are critical in real-world
applications spanning e-commerce, healthcare, and social media. To address
these tasks effectively, Relational Deep Learning (RDL) encodes relational data
as graphs, enabling Graph Neural Networks (GNNs) to exploit relational
structures for improved ... |
2502.06785 | DeepCrossAttention: Supercharging Transformer Residual Connections | cs.LG | Transformer networks have achieved remarkable success across diverse domains,
leveraging a variety of architectural innovations, including residual
connections. However, traditional residual connections, which simply sum the
outputs of previous layers, can dilute crucial information. This work
introduces DeepCrossAtt... |
2502.06786 | Matryoshka Quantization | cs.LG cs.AI | Quantizing model weights is critical for reducing the communication and
inference costs of large models. However, quantizing models -- especially to
low precisions like int4 or int2 -- requires a trade-off in model quality;
int2, in particular, is known to severely degrade model quality. Consequently,
practitioners a... |
2502.06787 | Visual Agentic AI for Spatial Reasoning with a Dynamic API | cs.CV | Visual reasoning -- the ability to interpret the visual world -- is crucial
for embodied agents that operate within three-dimensional scenes. Progress in
AI has led to vision and language models capable of answering questions from
images. However, their performance declines when tasked with 3D spatial
reasoning. To t... |
2502.06788 | EVEv2: Improved Baselines for Encoder-Free Vision-Language Models | cs.CV cs.AI | Existing encoder-free vision-language models (VLMs) are rapidly narrowing the
performance gap with their encoder-based counterparts, highlighting the
promising potential for unified multimodal systems with structural simplicity
and efficient deployment. We systematically clarify the performance gap between
VLMs using... |
2502.06789 | Information-theoretic Bayesian Optimization: Survey and Tutorial | cs.LG cs.AI cs.IT math.IT stat.ML | Several scenarios require the optimization of non-convex black-box functions,
that are noisy expensive to evaluate functions with unknown analytical
expression, whose gradients are hence not accessible. For example, the
hyper-parameter tuning problem of machine learning models. Bayesian
optimization is a class of met... |
2502.06798 | Prompt-Aware Scheduling for Efficient Text-to-Image Inferencing System | cs.LG cs.DC cs.GR | Traditional ML models utilize controlled approximations during high loads,
employing faster, but less accurate models in a process called accuracy
scaling. However, this method is less effective for generative text-to-image
models due to their sensitivity to input prompts and performance degradation
caused by large m... |
2502.06800 | Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer
Screening Among Populations in the United States: Machine Learning Approach | cs.LG stat.AP | Breast cancer screening plays a pivotal role in early detection and
subsequent effective management of the disease, impacting patient outcomes and
survival rates. This study aims to assess breast cancer screening rates
nationwide in the United States and investigate the impact of social
determinants of health on thes... |
2502.06802 | Solving the Content Gap in Roblox Game Recommendations: LLM-Based
Profile Generation and Reranking | cs.IR cs.AI cs.CL cs.LG | With the vast and dynamic user-generated content on Roblox, creating
effective game recommendations requires a deep understanding of game content.
Traditional recommendation models struggle with the inconsistent and sparse
nature of game text features such as titles and descriptions. Recent
advancements in large lang... |
2502.06803 | Emotion Recognition and Generation: A Comprehensive Review of Face,
Speech, and Text Modalities | cs.LG cs.AI cs.CV | Emotion recognition and generation have emerged as crucial topics in
Artificial Intelligence research, playing a significant role in enhancing
human-computer interaction within healthcare, customer service, and other
fields. Although several reviews have been conducted on emotion recognition and
generation as separat... |
2502.06805 | Efficient Diffusion Models: A Survey | cs.LG cs.GR | Diffusion models have emerged as powerful generative models capable of
producing high-quality contents such as images, videos, and audio,
demonstrating their potential to revolutionize digital content creation.
However, these capabilities come at the cost of their significant computational
resources and lengthy gener... |
2502.06806 | Logits are All We Need to Adapt Closed Models | cs.LG cs.AI cs.CL | Many commercial Large Language Models (LLMs) are often closed-source,
limiting developers to prompt tuning for aligning content generation with
specific applications. While these models currently do not provide access to
token logits, we argue that if such access were available, it would enable more
powerful adaptati... |
2502.06807 | Competitive Programming with Large Reasoning Models | cs.LG cs.AI cs.CL | We show that reinforcement learning applied to large language models (LLMs)
significantly boosts performance on complex coding and reasoning tasks.
Additionally, we compare two general-purpose reasoning models - OpenAI o1 and
an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses
hand-engineere... |
2502.06808 | On the Benefits of Attribute-Driven Graph Domain Adaptation | cs.LG cs.AI | Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network
learning, particularly pertinent due to the absence of labeled data in
real-world graph datasets. Recent studies attempted to learn domain invariant
representations by eliminating structural shifts between graphs. In this work,
we show that... |
2502.06809 | Neurons Speak in Ranges: Breaking Free from Discrete Neuronal
Attribution | cs.LG cs.AI cs.CL | Interpreting and controlling the internal mechanisms of large language models
(LLMs) is crucial for improving their trustworthiness and utility. Recent
efforts have primarily focused on identifying and manipulating neurons by
establishing discrete mappings between neurons and semantic concepts. However,
such mappings... |
2502.06810 | Emergence of Self-Awareness in Artificial Systems: A Minimalist
Three-Layer Approach to Artificial Consciousness | q-bio.NC cs.AI | This paper proposes a minimalist three-layer model for artificial
consciousness, focusing on the emergence of self-awareness. The model comprises
a Cognitive Integration Layer, a Pattern Prediction Layer, and an Instinctive
Response Layer, interacting with Access-Oriented and Pattern-Integrated Memory
systems. Unlike... |
2502.06811 | Aligning Human and Machine Attention for Enhanced Supervised Learning | cs.LG cs.AI cs.CL | Attention, or prioritization of certain information items over others, is a
critical element of any learning process, for both humans and machines. Given
that humans continue to outperform machines in certain learning tasks, it seems
plausible that machine performance could be enriched by aligning machine
attention w... |
2502.06812 | Harness Local Rewards for Global Benefits: Effective Text-to-Video
Generation Alignment with Patch-level Reward Models | cs.LG cs.GR | The emergence of diffusion models (DMs) has significantly improved the
quality of text-to-video generation models (VGMs). However, current VGM
optimization primarily emphasizes the global quality of videos, overlooking
localized errors, which leads to suboptimal generation capabilities. To address
this issue, we prop... |
2502.06813 | Policy Guided Tree Search for Enhanced LLM Reasoning | cs.LG cs.AI | Despite their remarkable capabilities, large language models often struggle
with tasks requiring complex reasoning and planning. While existing approaches
like Chain-of-Thought prompting and tree search techniques show promise, they
are limited by their reliance on predefined heuristics and computationally
expensive ... |
2502.06814 | Diffusion Instruction Tuning | cs.LG cs.AI cs.GR | We introduce Lavender, a simple supervised fine-tuning (SFT) method that
boosts the performance of advanced vision-language models (VLMs) by leveraging
state-of-the-art image generation models such as Stable Diffusion.
Specifically, Lavender aligns the text-vision attention in the VLM transformer
with the equivalent ... |
2502.06815 | Honegumi: An Interface for Accelerating the Adoption of Bayesian
Optimization in the Experimental Sciences | cs.LG cond-mat.mtrl-sci | Bayesian optimization (BO) has emerged as a powerful tool for guiding
experimental design and decision-making in various scientific fields, including
materials science, chemistry, and biology. However, despite its growing
popularity, the complexity of existing BO libraries and the steep learning
curve associated with... |
2502.06816 | DeepCell: Multiview Representation Learning for Post-Mapping Netlists | cs.LG cs.AI | Representation learning for post-mapping (PM) netlists is a critical
challenge in Electronic Design Automation (EDA), driven by the diverse and
complex nature of modern circuit designs. Existing approaches focus on
intermediate representations like And-Inverter Graphs (AIGs), limiting their
applicability to post-synt... |
2502.06817 | Diffusion-empowered AutoPrompt MedSAM | eess.IV cs.GR cs.LG | MedSAM, a medical foundation model derived from the SAM architecture, has
demonstrated notable success across diverse medical domains. However, its
clinical application faces two major challenges: the dependency on
labor-intensive manual prompt generation, which imposes a significant burden on
clinicians, and the abs... |
2502.06818 | Globality Strikes Back: Rethinking the Global Knowledge of CLIP in
Training-Free Open-Vocabulary Semantic Segmentation | cs.LG | Recent works modify CLIP to perform open-vocabulary semantic segmentation in
a training-free manner (TF-OVSS). In CLIP, patch-wise image representations
mainly encode the homogeneous image-level properties and thus are not
discriminative enough, hindering its application to the dense prediction task.
Previous works m... |
2502.06819 | Functional 3D Scene Synthesis through Human-Scene Optimization | cs.LG cs.GR | This paper presents a novel generative approach that outputs 3D indoor
environments solely from a textual description of the scene. Current methods
often treat scene synthesis as a mere layout prediction task, leading to rooms
with overlapping objects or overly structured scenes, with limited
consideration of the pra... |
2502.06820 | LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient
Fine-Tuning | cs.LG cs.AI | Low-rank adaptation (LoRA) has become a prevalent method for adapting
pre-trained large language models to downstream tasks. However, the simple
low-rank decomposition form may constrain the hypothesis space. To address this
limitation, we introduce Location-aware Cosine Adaptation (LoCA), a novel
frequency-domain pa... |
2502.06822 | DiffListener: Discrete Diffusion Model for Listener Generation | cs.LG cs.CL cs.GR | The listener head generation (LHG) task aims to generate natural nonverbal
listener responses based on the speaker's multimodal cues. While prior work
either rely on limited modalities (e.g. audio and facial information) or employ
autoregressive approaches which have limitations such as accumulating
prediction errors... |
2502.06823 | CTR-Driven Advertising Image Generation with Multimodal Large Language
Models | cs.LG cs.CV cs.GR cs.IR | In web data, advertising images are crucial for capturing user attention and
improving advertising effectiveness. Most existing methods generate background
for products primarily focus on the aesthetic quality, which may fail to
achieve satisfactory online performance. To address this limitation, we explore
the use o... |
2502.06824 | Neural Network-based Vehicular Channel Estimation Performance: Effect of
Noise in the Training Set | cs.LG cs.AI | Vehicular communication systems face significant challenges due to high
mobility and rapidly changing environments, which affect the channel over which
the signals travel. To address these challenges, neural network (NN)-based
channel estimation methods have been suggested. These methods are primarily
trained on high... |
2502.06825 | RLOMM: An Efficient and Robust Online Map Matching Framework with
Reinforcement Learning | cs.LG cs.DB | Online map matching is a fundamental problem in location-based services,
aiming to incrementally match trajectory data step-by-step onto a road network.
However, existing methods fail to meet the needs for efficiency, robustness,
and accuracy required by large-scale online applications, making this task
still a chall... |
2502.06826 | Transferring Graph Neural Networks for Soft Sensor Modeling using
Process Topologies | cs.LG cs.AI | Data-driven soft sensors help in process operations by providing real-time
estimates of otherwise hard- to-measure process quantities, e.g., viscosities
or product concentrations. Currently, soft sensors need to be developed
individually per plant. Using transfer learning, machine learning-based soft
sensors could be... |
2502.06827 | Learning to Synthesize Compatible Fashion Items Using Semantic Alignment
and Collocation Classification: An Outfit Generation Framework | cs.LG cs.AI cs.GR | The field of fashion compatibility learning has attracted great attention
from both the academic and industrial communities in recent years. Many studies
have been carried out for fashion compatibility prediction, collocated outfit
recommendation, artificial intelligence (AI)-enabled compatible fashion design,
and re... |
2502.06828 | Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding:
Insights from a Large-Scale Longitudinal Study | cs.LG cs.AI | This study investigates continual fine-tuning strategies for deep learning in
online longitudinal electroencephalography (EEG) motor imagery (MI) decoding
within a causal setting involving a large user group and multiple sessions per
participant. We are the first to explore such strategies across a large user
group, ... |
2502.06829 | Convolution-Based Converter : A Weak-Prior Approach For Modeling
Stochastic Processes Based On Conditional Density Estimation | cs.LG cs.AI | In this paper, a Convolution-Based Converter (CBC) is proposed to develop a
methodology for removing the strong or fixed priors in estimating the
probability distribution of targets based on observations in the stochastic
process. Traditional approaches, e.g., Markov-based and Gaussian process-based
methods, typicall... |
2502.06830 | OrderFusion: Encoding Orderbook for Probabilistic Intraday Price
Prediction | q-fin.CP cs.AI cs.LG | Efficient and reliable probabilistic prediction of intraday electricity
prices is essential to manage market uncertainties and support robust trading
strategies. However, current methods often suffer from parameter
inefficiencies, as they fail to fully exploit the potential of modeling
interdependencies between bids ... |
2502.06831 | No Location Left Behind: Measuring and Improving the Fairness of
Implicit Representations for Earth Data | cs.LG cs.AI | Implicit neural representations (INRs) exhibit growing promise in addressing
Earth representation challenges, ranging from emissions monitoring to climate
modeling. However, existing methods disproportionately prioritize global
average performance, whereas practitioners require fine-grained insights to
understand bia... |
2502.06832 | Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model
Approach | cs.LG cs.AI | Mixture of Experts (MoE) have shown remarkable success in leveraging
specialized expert networks for complex machine learning tasks. However, their
susceptibility to adversarial attacks presents a critical challenge for
deployment in robust applications. This paper addresses the critical question
of how to incorporat... |
2502.06833 | Entropy Adaptive Decoding: Dynamic Model Switching for Efficient
Inference | cs.LG cs.AI cs.CL | We present Entropy Adaptive Decoding (EAD), a novel approach for efficient
language model inference that dynamically switches between different-sized
models based on prediction uncertainty. By monitoring rolling entropy in model
logit distributions, our method identifies text regions where a smaller model
suffices an... |
2502.06834 | A Unified Knowledge-Distillation and Semi-Supervised Learning Framework
to Improve Industrial Ads Delivery Systems | cs.LG cs.AI | Industrial ads ranking systems conventionally rely on labeled impression
data, which leads to challenges such as overfitting, slower incremental gain
from model scaling, and biases due to discrepancies between training and
serving data. To overcome these issues, we propose a Unified framework for
Knowledge-Distillati... |
2502.06835 | Reinforcement Learning on AYA Dyads to Enhance Medication Adherence | cs.LG | Medication adherence is critical for the recovery of adolescents and young
adults (AYAs) who have undergone hematopoietic cell transplantation (HCT).
However, maintaining adherence is challenging for AYAs after hospital
discharge, who experience both individual (e.g. physical and emotional
symptoms) and interpersonal... |
2502.06836 | CAST: Cross Attention based multimodal fusion of Structure and Text for
materials property prediction | cs.LG cond-mat.mtrl-sci cs.AI | Recent advancements in AI have revolutionized property prediction in
materials science and accelerating material discovery. Graph neural networks
(GNNs) stand out due to their ability to represent crystal structures as
graphs, effectively capturing local interactions and delivering superior
predictions. However, thes... |
2502.06837 | Comparison of CNN-based deep learning architectures for unsteady CFD
acceleration on small datasets | cs.LG physics.flu-dyn | CFD acceleration for virtual nuclear reactors or digital twin technology is a
primary goal in the nuclear industry. This study compares advanced
convolutional neural network (CNN) architectures for accelerating unsteady
computational fluid dynamics (CFD) simulations using small datasets based on a
challenging natural... |
2502.06838 | TorchResist: Open-Source Differentiable Resist Simulator | cs.LG | Recent decades have witnessed remarkable advancements in artificial
intelligence (AI), including large language models (LLMs), image and video
generative models, and embodied AI systems. These advancements have led to an
explosive increase in the demand for computational power, challenging the
limits of Moore's Law. ... |
2502.06839 | A Hybrid Model for Weakly-Supervised Speech Dereverberation | eess.AS cs.AI cs.SD eess.SP | This paper introduces a new training strategy to improve speech
dereverberation systems using minimal acoustic information and reverberant
(wet) speech. Most existing algorithms rely on paired dry/wet data, which is
difficult to obtain, or on target metrics that may not adequately capture
reverberation characteristic... |
2502.06842 | Integrating Generative Artificial Intelligence in ADRD: A Framework for
Streamlining Diagnosis and Care in Neurodegenerative Diseases | cs.CY cs.AI | Healthcare systems are struggling to meet the growing demand for neurological
care, with challenges particularly acute in Alzheimer's disease and related
dementias (ADRD). While artificial intelligence research has often focused on
identifying patterns beyond human perception, implementing such predictive
capabilitie... |
2502.06843 | Vision-Integrated LLMs for Autonomous Driving Assistance : Human
Performance Comparison and Trust Evaluation | cs.CV cs.AI cs.HC | Traditional autonomous driving systems often struggle with reasoning in
complex, unexpected scenarios due to limited comprehension of spatial
relationships. In response, this study introduces a Large Language Model
(LLM)-based Autonomous Driving (AD) assistance system that integrates a vision
adapter and an LLM reaso... |
2502.06844 | Exploring Model Invariance with Discrete Search for Ultra-Low-Bit
Quantization | cs.LG cs.AI cs.CL | Large language models have been increasing in size due to their success in a
wide range of applications. This calls for a pressing need to reduce memory
usage to make them more accessible. Post-training quantization is a popular
technique which uses fewer bits (e.g., 4--8 bits) to represent the model
without retraini... |
2502.06845 | DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits | physics.ins-det cs.AI cs.LG | Nuclear Magnetic Resonance (NMR) spectroscopy is a crucial analytical
technique used for molecular structure elucidation, with applications spanning
chemistry, biology, materials science, and medicine. However, the frequency
resolution of NMR spectra is limited by the "field strength" of the instrument.
High-field NM... |
2502.06846 | Prot2Chat: Protein LLM with Early Fusion of Sequence and Structure | cs.LG cs.AI q-bio.BM | Proteins play a pivotal role in living organisms, yet understanding their
functions presents significant challenges, including the limited flexibility of
classification-based methods, the inability to effectively leverage spatial
structural information, and the lack of systematic evaluation metrics for
protein Q&A sy... |
2502.06847 | A Deep Learning Framework Integrating CNN and BiLSTM for Financial
Systemic Risk Analysis and Prediction | cs.LG cs.CE | This study proposes a deep learning model based on the combination of
convolutional neural network (CNN) and bidirectional long short-term memory
network (BiLSTM) for discriminant analysis of financial systemic risk. The
model first uses CNN to extract local patterns of multidimensional features of
financial markets,... |
2502.06848 | Transfer learning in Scalable Graph Neural Network for Improved Physical
Simulation | cs.LG cs.AI | In recent years, Graph Neural Network (GNN) based models have shown promising
results in simulating physics of complex systems. However, training dedicated
graph network based physics simulators can be costly, as most models are
confined to fully supervised training, which requires extensive data generated
from tradi... |
2502.06849 | Model Fusion via Neuron Transplantation | cs.LG cs.AI | Ensemble learning is a widespread technique to improve the prediction
performance of neural networks. However, it comes at the price of increased
memory and inference time. In this work we propose a novel model fusion
technique called \emph{Neuron Transplantation (NT)} in which we fuse an
ensemble of models by transp... |
2502.06851 | Survey on Vision-Language-Action Models | cs.CL cs.AI cs.CV | This paper presents an AI-generated review of Vision-Language-Action (VLA)
models, summarizing key methodologies, findings, and future directions. The
content is produced using large language models (LLMs) and is intended only for
demonstration purposes. This work does not represent original research, but
highlights ... |
2502.06852 | EAP-GP: Mitigating Saturation Effect in Gradient-based Automated Circuit
Identification | cs.LG cs.AI | Understanding the internal mechanisms of transformer-based language models
remains challenging. Mechanistic interpretability based on circuit discovery
aims to reverse engineer neural networks by analyzing their internal processes
at the level of computational subgraphs. In this paper, we revisit existing
gradient-ba... |
2502.06853 | Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian
Neural Networks | cs.LG cs.AI | Over the past decade, the investigation of machine learning (ML) within the
field of nuclear engineering has grown significantly. With many approaches
reaching maturity, the next phase of investigation will determine the
feasibility and usefulness of ML model implementation in a production setting.
Several of the cod... |
2502.06854 | Can Large Language Models Understand Intermediate Representations? | cs.LG cs.AI cs.CL | Intermediate Representations (IRs) are essential in compiler design and
program analysis, yet their comprehension by Large Language Models (LLMs)
remains underexplored. This paper presents a pioneering empirical study to
investigate the capabilities of LLMs, including GPT-4, GPT-3, Gemma 2, LLaMA
3.1, and Code Llama,... |
2502.06855 | Self-Supervised Prompt Optimization | cs.CL cs.AI cs.LG | Well-designed prompts are crucial for enhancing Large language models' (LLMs)
reasoning capabilities while aligning their outputs with task requirements
across diverse domains. However, manually designed prompts require expertise
and iterative experimentation. While existing prompt optimization methods aim
to automat... |
2502.06857 | Gemstones: A Model Suite for Multi-Faceted Scaling Laws | cs.LG cs.AI | Scaling laws are typically fit using a family of models with a narrow range
of frozen hyper-parameter choices. In this work we study scaling laws using a
wide range of architecture and hyper-parameter choices, and highlight their
impact on resulting prescriptions. As a primary artifact of our research, we
release the... |
2502.06858 | LLM-Supported Natural Language to Bash Translation | cs.CL cs.AI | The Bourne-Again Shell (Bash) command-line interface for Linux systems has
complex syntax and requires extensive specialized knowledge. Using the natural
language to Bash command (NL2SH) translation capabilities of large language
models (LLMs) for command composition circumvents these issues. However, the
NL2SH perfo... |
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