id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.00687 | A Flexible Precision Scaling Deep Neural Network Accelerator with
Efficient Weight Combination | cs.AR cs.SY eess.SY | Deploying mixed-precision neural networks on edge devices is friendly to
hardware resources and power consumption. To support fully mixed-precision
neural network inference, it is necessary to design flexible hardware
accelerators for continuous varying precision operations. However, the previous
works have issues on... |
2502.00688 | High-Order Matching for One-Step Shortcut Diffusion Models | cs.CV cs.AI cs.LG | One-step shortcut diffusion models [Frans, Hafner, Levine and Abbeel, ICLR
2025] have shown potential in vision generation, but their reliance on
first-order trajectory supervision is fundamentally limited. The Shortcut
model's simplistic velocity-only approach fails to capture intrinsic manifold
geometry, leading to... |
2502.00690 | Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis
of Fairness in AI Conference Policies | cs.LG cs.AI cs.CY cs.DL | As AI research surges in both impact and volume, conferences have imposed
submission limits to maintain paper quality and alleviate organizational
pressure. In this work, we examine the fairness of desk-rejection systems under
submission limits and reveal that existing practices can result in substantial
inequities. ... |
2502.00691 | Learning Autonomous Code Integration for Math Language Models | cs.AI cs.CL cs.LG | Recent advances in mathematical problem-solving with language models (LMs)
integrate chain-of-thought (CoT) reasoning and code execution to harness their
complementary strengths. However, existing hybrid frameworks exhibit a critical
limitation: they depend on externally dictated instructions or rigid
code-integratio... |
2502.00694 | Leveraging Large Language Models to Predict Antibody Biological Activity
Against Influenza A Hemagglutinin | cs.LG cs.AI q-bio.QM | Monoclonal antibodies (mAbs) represent one of the most prevalent FDA-approved
modalities for treating autoimmune diseases, infectious diseases, and cancers.
However, discovery and development of therapeutic antibodies remains a
time-consuming and expensive process. Recent advancements in machine learning
(ML) and art... |
2502.00695 | TMI-CLNet: Triple-Modal Interaction Network for Chronic Liver Disease
Prognosis From Imaging, Clinical, and Radiomic Data Fusion | cs.CV cs.AI | Chronic liver disease represents a significant health challenge worldwide and
accurate prognostic evaluations are essential for personalized treatment plans.
Recent evidence suggests that integrating multimodal data, such as computed
tomography imaging, radiomic features, and clinical information, can provide
more co... |
2502.00698 | MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal
Models | cs.AI cs.CV | IQ testing has served as a foundational methodology for evaluating human
cognitive capabilities, deliberately decoupling assessment from linguistic
background, language proficiency, or domain-specific knowledge to isolate core
competencies in abstraction and reasoning. Yet, artificial intelligence
research currently ... |
2502.00700 | S2CFormer: Reorienting Learned Image Compression from Spatial
Interaction to Channel Aggregation | cs.CV eess.IV | Transformers have achieved significant success in learned image compression
(LIC), with Swin Transformers emerging as the mainstream choice for nonlinear
transforms. A common belief is that their sophisticated spatial operations
contribute most to their efficacy. However, the crucial role of the
feed-forward network ... |
2502.00705 | Optimization for Neural Operators can Benefit from Width | cs.LG math.OC | Neural Operators that directly learn mappings between function spaces, such
as Deep Operator Networks (DONs) and Fourier Neural Operators (FNOs), have
received considerable attention. Despite the universal approximation guarantees
for DONs and FNOs, there is currently no optimization convergence guarantee for
learnin... |
2502.00706 | Model Provenance Testing for Large Language Models | cs.CR cs.CL cs.LG | Large language models are increasingly customized through fine-tuning and
other adaptations, creating challenges in enforcing licensing terms and
managing downstream impacts. Tracking model origins is crucial both for
protecting intellectual property and for identifying derived models when biases
or vulnerabilities a... |
2502.00708 | PhiP-G: Physics-Guided Text-to-3D Compositional Scene Generation | cs.CV cs.AI | Text-to-3D asset generation has achieved significant optimization under the
supervision of 2D diffusion priors. However, when dealing with compositional
scenes, existing methods encounter several challenges: 1). failure to ensure
that composite scene layouts comply with physical laws; 2). difficulty in
accurately cap... |
2502.00709 | RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large
Language Models | cs.IR | In an Information Retrieval (IR) system, reranking plays a critical role by
sorting candidate passages according to their relevance to a specific query.
This process demands a nuanced understanding of the variations among passages
linked to the query. In this work, we introduce RankFlow, a multi-role
reranking workfl... |
2502.00711 | VIKSER: Visual Knowledge-Driven Self-Reinforcing Reasoning Framework | cs.CV cs.AI | Visual reasoning refers to the task of solving questions about visual
information. Current visual reasoning methods typically employ pre-trained
vision-language model (VLM) strategies or deep neural network approaches.
However, existing efforts are constrained by limited reasoning
interpretability, while hindering by... |
2502.00712 | Registration-Enhanced Segmentation Method for Prostate Cancer in
Ultrasound Images | eess.IV cs.AI cs.CV | Prostate cancer is a major cause of cancer-related deaths in men, where early
detection greatly improves survival rates. Although MRI-TRUS fusion biopsy
offers superior accuracy by combining MRI's detailed visualization with TRUS's
real-time guidance, it is a complex and time-intensive procedure that relies
heavily o... |
2502.00714 | Harnessing Discrete Differential Geometry: A Virtual Playground for the
Bilayer Soft Robotics | cs.RO cond-mat.soft | Soft robots have garnered significant attention due to their promising
applications across various domains. A hallmark of these systems is their
bilayer structure, where strain mismatch caused by differential expansion
between layers induces complex deformations. Despite progress in theoretical
modeling and numerical... |
2502.00716 | UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node
Classification | cs.LG | Graph-structured datasets often suffer from class imbalance, which
complicates node classification tasks. In this work, we address this issue by
first providing an upper bound on population risk for imbalanced transductive
node classification. We then propose a simple and novel algorithm,
Uncertainty-aware Pseudo-lab... |
2502.00717 | MINT: Mitigating Hallucinations in Large Vision-Language Models via
Token Reduction | cs.CV | Hallucination has been a long-standing and inevitable problem that hinders
the application of Large Vision-Language Models (LVLMs) in domains that require
high reliability. Various methods focus on improvement depending on data
annotations or training strategies, yet place less emphasis on LLM's inherent
problems. To... |
2502.00718 | "I am bad": Interpreting Stealthy, Universal and Robust Audio Jailbreaks
in Audio-Language Models | cs.LG cs.SD eess.AS | The rise of multimodal large language models has introduced innovative
human-machine interaction paradigms but also significant challenges in machine
learning safety. Audio-Language Models (ALMs) are especially relevant due to
the intuitive nature of spoken communication, yet little is known about their
failure modes... |
2502.00719 | Vision and Language Reference Prompt into SAM for Few-shot Segmentation | cs.CV | Segment Anything Model (SAM) represents a large-scale segmentation model that
enables powerful zero-shot capabilities with flexible prompts. While SAM can
segment any object in zero-shot, it requires user-provided prompts for each
target image and does not attach any label information to masks. Few-shot
segmentation ... |
2502.00724 | Learned Bayesian Cram\'er-Rao Bound for Unknown Measurement Models Using
Score Neural Networks | eess.SP cs.AI cs.LG stat.ML | The Bayesian Cram\'er-Rao bound (BCRB) is a crucial tool in signal processing
for assessing the fundamental limitations of any estimation problem as well as
benchmarking within a Bayesian frameworks. However, the BCRB cannot be computed
without full knowledge of the prior and the measurement distributions. In this
wo... |
2502.00725 | Understanding and Mitigating the High Computational Cost in Path Data
Diffusion | cs.LG | Advancements in mobility services, navigation systems, and smart
transportation technologies have made it possible to collect large amounts of
path data. Modeling the distribution of this path data, known as the Path
Generation (PG) problem, is crucial for understanding urban mobility patterns
and developing intellig... |
2502.00726 | Perspectives for Direct Interpretability in Multi-Agent Deep
Reinforcement Learning | cs.AI | Multi-Agent Deep Reinforcement Learning (MADRL) was proven efficient in
solving complex problems in robotics or games, yet most of the trained models
are hard to interpret. While learning intrinsically interpretable models
remains a prominent approach, its scalability and flexibility are limited in
handling complex t... |
2502.00728 | Meta-Prompt Optimization for LLM-Based Sequential Decision Making | cs.LG | Large language models (LLMs) have recently been employed as agents to solve
sequential decision-making tasks such as Bayesian optimization and multi-armed
bandits (MAB). These works usually adopt an LLM for sequential action selection
by providing it with a fixed, manually designed meta-prompt. However, numerous
prev... |
2502.00729 | Selective Response Strategies for GenAI | cs.AI cs.GT cs.SI | The rise of Generative AI (GenAI) has significantly impacted human-based
forums like Stack Overflow, which are essential for generating high-quality
data. This creates a negative feedback loop, hindering the development of GenAI
systems, which rely on such data to provide accurate responses. In this paper,
we provide... |
2502.00730 | Spatio-Temporal Progressive Attention Model for EEG Classification in
Rapid Serial Visual Presentation Task | cs.CV | As a type of multi-dimensional sequential data, the spatial and temporal
dependencies of electroencephalogram (EEG) signals should be further
investigated. Thus, in this paper, we propose a novel spatial-temporal
progressive attention model (STPAM) to improve EEG classification in rapid
serial visual presentation (RS... |
2502.00734 | CycleGuardian: A Framework for Automatic RespiratorySound classification
Based on Improved Deep clustering and Contrastive Learning | cs.SD cs.AI eess.AS | Auscultation plays a pivotal role in early respiratory and pulmonary disease
diagnosis. Despite the emergence of deep learning-based methods for automatic
respiratory sound classification post-Covid-19, limited datasets impede
performance enhancement. Distinguishing between normal and abnormal respiratory
sounds pose... |
2502.00735 | `Do as I say not as I do': A Semi-Automated Approach for Jailbreak
Prompt Attack against Multimodal LLMs | cs.CR cs.AI cs.SE | Large Language Models (LLMs) have seen widespread applications across various
domains due to their growing ability to process diverse types of input data,
including text, audio, image and video. While LLMs have demonstrated
outstanding performance in understanding and generating contexts for different
scenarios, they... |
2502.00737 | Scalable Sobolev IPM for Probability Measures on a Graph | stat.ML cs.LG | We investigate the Sobolev IPM problem for probability measures supported on
a graph metric space. Sobolev IPM is an important instance of integral
probability metrics (IPM), and is obtained by constraining a critic function
within a unit ball defined by the Sobolev norm. In particular, it has been used
to compare pr... |
2502.00739 | Orlicz-Sobolev Transport for Unbalanced Measures on a Graph | stat.ML cs.LG | Moving beyond $L^p$ geometric structure, Orlicz-Wasserstein (OW) leverages a
specific class of convex functions for Orlicz geometric structure. While OW
remarkably helps to advance certain machine learning approaches, it has a high
computational complexity due to its two-level optimization formula. Recently,
Le et al... |
2502.00744 | CoNNect: A Swiss-Army-Knife Regularizer for Pruning of Neural Networks | cs.LG | Pruning encompasses a range of techniques aimed at increasing the sparsity of
neural networks (NNs). These techniques can generally be framed as minimizing a
loss function subject to an $L_0$-norm constraint. This paper introduces
CoNNect, a novel differentiable regularizer for sparse NN training that ensures
connect... |
2502.00745 | BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit
Classifiers as Experts | cs.LG cs.CL cs.CV | Early Exit (EE) techniques have emerged as a means to reduce inference
latency in Deep Neural Networks (DNNs). The latency improvement and accuracy in
these techniques crucially depend on the criteria used to make exit decisions.
We propose a new decision criterion where exit classifiers are treated as
experts BEEM a... |
2502.00747 | Universal Post-Processing Networks for Joint Optimization of Modules in
Task-Oriented Dialogue Systems | cs.CL cs.AI | Post-processing networks (PPNs) are components that modify the outputs of
arbitrary modules in task-oriented dialogue systems and are optimized using
reinforcement learning (RL) to improve the overall task completion capability
of the system. However, previous PPN-based approaches have been limited to
handling only a... |
2502.00749 | An Event-Based Perception Pipeline for a Table Tennis Robot | cs.RO cs.CV | Table tennis robots gained traction over the last years and have become a
popular research challenge for control and perception algorithms. Fast and
accurate ball detection is crucial for enabling a robotic arm to rally the ball
back successfully. So far, most table tennis robots use conventional,
frame-based cameras... |
2502.00752 | Zero-Shot Warning Generation for Misinformative Multimodal Content | cs.AI cs.CL cs.IR | The widespread prevalence of misinformation poses significant societal
concerns. Out-of-context misinformation, where authentic images are paired with
false text, is particularly deceptive and easily misleads audiences. Most
existing detection methods primarily evaluate image-text consistency but often
lack sufficien... |
2502.00753 | Mirror Descent Under Generalized Smoothness | math.OC cs.LG | Smoothness is crucial for attaining fast rates in first-order optimization.
However, many optimization problems in modern machine learning involve
non-smooth objectives. Recent studies relax the smoothness assumption by
allowing the Lipschitz constant of the gradient to grow with respect to the
gradient norm, which a... |
2502.00754 | Continuity-Preserving Convolutional Autoencoders for Learning Continuous
Latent Dynamical Models from Images | cs.LG cs.CV | Continuous dynamical systems are cornerstones of many scientific and
engineering disciplines. While machine learning offers powerful tools to model
these systems from trajectory data, challenges arise when these trajectories
are captured as images, resulting in pixel-level observations that are discrete
in nature. Co... |
2502.00757 | AgentBreeder: Mitigating the AI Safety Impact of Multi-Agent Scaffolds | cs.CR cs.AI cs.NE | Scaffolding Large Language Models (LLMs) into multi-agent systems often
improves performance on complex tasks, but the safety impact of such scaffolds
has not been as thoroughly explored. In this paper, we introduce AGENTBREEDER a
framework for multi-objective evolutionary search over scaffolds. Our
REDAGENTBREEDER e... |
2502.00758 | Structural Latency Perturbation in Large Language Models Through
Recursive State Induction | cs.CL | Computational efficiency has remained a critical consideration in scaling
high-capacity language models, with inference latency and resource consumption
presenting significant constraints on real-time applications. The study has
introduced a structured latency perturbation mechanism that modifies
computational pathwa... |
2502.00760 | Privacy Preserving Properties of Vision Classifiers | cs.LG cs.CR cs.CV | Vision classifiers are often trained on proprietary datasets containing
sensitive information, yet the models themselves are frequently shared openly
under the privacy-preserving assumption. Although these models are assumed to
protect sensitive information in their training data, the extent to which this
assumption ... |
2502.00761 | FIRE: Flexible Integration of Data Quality Ratings for Effective
Pre-Training | cs.CL | Selecting high-quality data can significantly improve the pretraining
efficiency of large language models (LLMs). Existing methods generally rely on
heuristic techniques and single-quality signals, limiting their ability to
evaluate data quality comprehensively. In this work, we propose FIRE, a
flexible and scalable ... |
2502.00762 | On Overlap Ratio in Defocused Electron Ptychography | eess.SP cs.IR physics.app-ph physics.med-ph | Four-dimensional Scanning Transmission Electron Microscopy (4D STEM) with
data acquired using a defocused electron probe is a promising tool for
characterising complex biological specimens and materials through a phase
retrieval process known as Electron Ptychography (EP). The efficacy of 4D STEM
acquisition and the ... |
2502.00767 | Learning-Based TSP-Solvers Tend to Be Overly Greedy | cs.LG cs.AI cs.DS | Deep learning has shown significant potential in solving combinatorial
optimization problems such as the Euclidean traveling salesman problem (TSP).
However, most training and test instances for existing TSP algorithms are
generated randomly from specific distributions like uniform distribution. This
has led to a lac... |
2502.00775 | ATA: Adaptive Task Allocation for Efficient Resource Management in
Distributed Machine Learning | cs.LG cs.DC math.OC stat.ML | Asynchronous methods are fundamental for parallelizing computations in
distributed machine learning. They aim to accelerate training by fully
utilizing all available resources. However, their greedy approach can lead to
inefficiencies using more computation than required, especially when
computation times vary across... |
2502.00779 | Role of Mixup in Topological Persistence Based Knowledge Distillation
for Wearable Sensor Data | cs.LG cs.AI eess.SP | The analysis of wearable sensor data has enabled many successes in several
applications. To represent the high-sampling rate time-series with sufficient
detail, the use of topological data analysis (TDA) has been considered, and it
is found that TDA can complement other time-series features. Nonetheless, due
to the l... |
2502.00780 | Constructing Fundamentals for the Theory of Proportions and Symbolic
Allusions Applied Interdisciplinarily | cs.IT math.IT q-bio.NC | The Theory of Proportions and Symbolic Allusions applied Interdisciplinary
(TPASAI) is a framework that integrates mathematics, linguistics, psychology,
and game theory to uncover hidden patterns and proportions in reality. Its
central idea is that numerical encoding of symbols, dates, and language can
reveal recurri... |
2502.00782 | Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning,
Lightweight Fine-Tuning, and Low-Rank Adaptation | cs.LG | AI for PDEs has garnered significant attention, particularly Physics-Informed
Neural Networks (PINNs). However, PINNs are typically limited to solving
specific problems, and any changes in problem conditions necessitate
retraining. Therefore, we explore the generalization capability of transfer
learning in the strong... |
2502.00783 | A method for estimating forest carbon storage distribution density via
artificial intelligence generated content model | cs.CV eess.IV | Forest is the most significant land-based carbon storage mechanism. The
forest carbon sink can effectively decrease the atmospheric CO2 concentration
and mitigate climate change. Remote sensing estimation not only ensures high
accuracy of data, but also enables large-scale area observation. Optical images
provide the... |
2502.00784 | Estimating forest carbon stocks from high-resolution remote sensing
imagery by reducing domain shift with style transfer | cs.CV eess.IV | Forests function as crucial carbon reservoirs on land, and their carbon sinks
can efficiently reduce atmospheric CO2 concentrations and mitigate climate
change. Currently, the overall trend for monitoring and assessing forest carbon
stocks is to integrate ground monitoring sample data with satellite remote
sensing im... |
2502.00791 | Vision-centric Token Compression in Large Language Model | cs.CL cs.CV | Large Language Models (LLMs) have revolutionized natural language processing,
excelling in handling longer sequences. However, the inefficiency and
redundancy in processing extended in-context tokens remain a challenge. Many
attempts to address this rely on compressing tokens with smaller text encoders,
yet we questi... |
2502.00792 | RTBAgent: A LLM-based Agent System for Real-Time Bidding | cs.AI | Real-Time Bidding (RTB) enables advertisers to place competitive bids on
impression opportunities instantaneously, striving for cost-effectiveness in a
highly competitive landscape. Although RTB has widely benefited from the
utilization of technologies such as deep learning and reinforcement learning,
the reliability... |
2502.00795 | Data Fusion for Full-Range Response Reconstruction via Diffusion Models | cs.CE | Accurately capturing the full-range response of structures is crucial in
structural health monitoring (SHM) for ensuring safety and operational
integrity. However, limited sensor deployment due to cost, accessibility, or
scale often hinders comprehensive monitoring. This paper presents a novel data
fusion framework u... |
2502.00796 | Task-Specific Adaptation with Restricted Model Access | cs.CV | The emergence of foundational models has greatly improved performance across
various downstream tasks, with fine-tuning often yielding even better results.
However, existing fine-tuning approaches typically require access to model
weights and layers, leading to challenges such as managing multiple model
copies or inf... |
2502.00798 | Deep Neural Network for Phonon-Assisted Optical Spectra in
Semiconductors | cond-mat.mtrl-sci cs.LG | Phonon-assisted optical absorption in semiconductors is crucial for
understanding and optimizing optoelectronic devices, yet its accurate
simulation remains a significant challenge in computational materials science.
We present an efficient approach that combines deep learning tight-binding (TB)
and potential models ... |
2502.00800 | Adversarial Semantic Augmentation for Training Generative Adversarial
Networks under Limited Data | cs.CV eess.IV | Generative adversarial networks (GANs) have made remarkable achievements in
synthesizing images in recent years. Typically, training GANs requires massive
data, and the performance of GANs deteriorates significantly when training data
is limited. To improve the synthesis performance of GANs in low-data regimes,
exist... |
2502.00801 | Environment-Driven Online LiDAR-Camera Extrinsic Calibration | cs.CV cs.AI cs.RO | LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in
computer vision. Existing methods typically rely on customized calibration
targets or fixed scene types, lacking the flexibility to handle variations in
sensor data and environmental contexts. This paper introduces EdO-LCEC, the
first environmen... |
2502.00802 | Fisher-Guided Selective Forgetting: Mitigating The Primacy Bias in Deep
Reinforcement Learning | cs.LG cs.AI | Deep Reinforcement Learning (DRL) systems often tend to overfit to early
experiences, a phenomenon known as the primacy bias (PB). This bias can
severely hinder learning efficiency and final performance, particularly in
complex environments. This paper presents a comprehensive investigation of PB
through the lens of ... |
2502.00803 | ProPINN: Demystifying Propagation Failures in Physics-Informed Neural
Networks | cs.LG | Physics-informed neural networks (PINNs) have earned high expectations in
solving partial differential equations (PDEs), but their optimization usually
faces thorny challenges due to the unique derivative-dependent loss function.
By analyzing the loss distribution, previous research observed the propagation
failure p... |
2502.00806 | UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs | cs.LG | Existing foundation models, such as CLIP, aim to learn a unified embedding
space for multimodal data, enabling a wide range of downstream web-based
applications like search, recommendation, and content classification. However,
these models often overlook the inherent graph structures in multimodal
datasets, where ent... |
2502.00808 | Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage
in Downstream Applications | cs.LG cs.CR cs.CY | Large language models (LLMs) have facilitated the generation of high-quality,
cost-effective synthetic data for developing downstream models and conducting
statistical analyses in various domains. However, the increased reliance on
synthetic data may pose potential negative impacts. Numerous studies have
demonstrated... |
2502.00814 | Disentangling Length Bias In Preference Learning Via
Response-Conditioned Modeling | cs.LG cs.CL | Reinforcement Learning from Human Feedback (RLHF) has achieved considerable
success in aligning large language models (LLMs) by modeling human preferences
with a learnable reward model and employing a reinforcement learning algorithm
to maximize the reward model's scores. However, these reward models are
susceptible ... |
2502.00816 | Sundial: A Family of Highly Capable Time Series Foundation Models | cs.LG | We introduce Sundial, a family of native, flexible, and scalable time series
foundation models. To predict the next-patch's distribution, we propose a
TimeFlow Loss based on flow-matching, which facilitates native pre-training of
Transformers on time series without discrete tokenization. Conditioned on
arbitrary-leng... |
2502.00817 | Probing Large Language Models in Reasoning and Translating Complex
Linguistic Puzzles | cs.CL | This paper investigates the utilization of Large Language Models (LLMs) for
solving complex linguistic puzzles, a domain requiring advanced reasoning and
adept translation capabilities akin to human cognitive processes. We explore
specific prompting techniques designed to enhance ability of LLMs to reason and
elucida... |
2502.00818 | Error-quantified Conformal Inference for Time Series | stat.ML cs.LG | Uncertainty quantification in time series prediction is challenging due to
the temporal dependence and distribution shift on sequential data. Conformal
inference provides a pivotal and flexible instrument for assessing the
uncertainty of machine learning models through prediction sets. Recently, a
series of online co... |
2502.00820 | OOD Detection with immature Models | cs.LG cs.CV | Likelihood-based deep generative models (DGMs) have gained significant
attention for their ability to approximate the distributions of
high-dimensional data. However, these models lack a performance guarantee in
assigning higher likelihood values to in-distribution (ID) inputs, data the
models are trained on, compare... |
2502.00823 | Online Learning of Pure States is as Hard as Mixed States | quant-ph cs.LG | Quantum state tomography, the task of learning an unknown quantum state, is a
fundamental problem in quantum information. In standard settings, the
complexity of this problem depends significantly on the type of quantum state
that one is trying to learn, with pure states being substantially easier to
learn than gener... |
2502.00826 | Weak Supervision Dynamic KL-Weighted Diffusion Models Guided by Large
Language Models | cs.CL | In this paper, we presents a novel method for improving text-to-image
generation by combining Large Language Models (LLMs) with diffusion models, a
hybrid approach aimed at achieving both higher quality and efficiency in image
synthesis from text descriptions. Our approach introduces a new dynamic
KL-weighting strate... |
2502.00828 | Decision-informed Neural Networks with Large Language Model Integration
for Portfolio Optimization | q-fin.PM cs.AI q-fin.CP | This paper addresses the critical disconnect between prediction and decision
quality in portfolio optimization by integrating Large Language Models (LLMs)
with decision-focused learning. We demonstrate both theoretically and
empirically that minimizing the prediction error alone leads to suboptimal
portfolio decision... |
2502.00829 | A Comprehensive Analysis on LLM-based Node Classification Algorithms | cs.LG cs.SI | Node classification is a fundamental task in graph analysis, with broad
applications across various fields. Recent breakthroughs in Large Language
Models (LLMs) have enabled LLM-based approaches for this task. Although many
studies demonstrate the impressive performance of LLM-based methods, the lack
of clear design ... |
2502.00832 | Generalization of Medical Large Language Models through Cross-Domain
Weak Supervision | cs.CL | The advancement of large language models (LLMs) has opened new frontiers in
natural language processing, particularly in specialized domains like
healthcare. In this paper, we propose the Incremental Curriculum-Based
Fine-Tuning (ICFT) framework to enhance the generative capabilities of medical
large language models ... |
2502.00833 | Cross multiscale vision transformer for deep fake detection | cs.CV | The proliferation of deep fake technology poses significant challenges to
digital media authenticity, necessitating robust detection mechanisms. This
project evaluates deep fake detection using the SP Cup's 2025 deep fake
detection challenge dataset. We focused on exploring various deep learning
models for detecting ... |
2502.00834 | Boosting Adversarial Robustness and Generalization with Structural Prior | cs.LG cs.CR cs.NE | This work investigates a novel approach to boost adversarial robustness and
generalization by incorporating structural prior into the design of deep
learning models. Specifically, our study surprisingly reveals that existing
dictionary learning-inspired convolutional neural networks (CNNs) provide a
false sense of se... |
2502.00835 | CAIMAN: Causal Action Influence Detection for Sample Efficient
Loco-manipulation | cs.RO cs.LG | Enabling legged robots to perform non-prehensile loco-manipulation with large
and heavy objects is crucial for enhancing their versatility. However, this is
a challenging task, often requiring sophisticated planning strategies or
extensive task-specific reward shaping, especially in unstructured scenarios
with obstac... |
2502.00837 | Explainability in Practice: A Survey of Explainable NLP Across Various
Domains | cs.CL cs.AI | Natural Language Processing (NLP) has become a cornerstone in many critical
sectors, including healthcare, finance, and customer relationship management.
This is especially true with the development and use of advanced models such as
GPT-based architectures and BERT, which are widely used in decision-making
processes... |
2502.00840 | Activation Approximations Can Incur Safety Vulnerabilities Even in
Aligned LLMs: Comprehensive Analysis and Defense | cs.CR cs.AI | Large Language Models (LLMs) have showcased remarkable capabilities across
various domains. Accompanying the evolving capabilities and expanding
deployment scenarios of LLMs, their deployment challenges escalate due to their
sheer scale and the advanced yet complex activation designs prevalent in
notable model series... |
2502.00843 | VLM-Assisted Continual learning for Visual Question Answering in
Self-Driving | cs.CV | In this paper, we propose a novel approach for solving the Visual Question
Answering (VQA) task in autonomous driving by integrating Vision-Language
Models (VLMs) with continual learning. In autonomous driving, VQA plays a vital
role in enabling the system to understand and reason about its surroundings.
However, tra... |
2502.00846 | Federated Generalised Variational Inference: A Robust Probabilistic
Federated Learning Framework | cs.LG stat.ML | We introduce FedGVI, a probabilistic Federated Learning (FL) framework that
is provably robust to both prior and likelihood misspecification. FedGVI
addresses limitations in both frequentist and Bayesian FL by providing unbiased
predictions under model misspecification, with calibrated uncertainty
quantification. Our... |
2502.00847 | SecPE: Secure Prompt Ensembling for Private and Robust Large Language
Models | cs.CR cs.AI | With the growing popularity of LLMs among the general public users,
privacy-preserving and adversarial robustness have become two pressing demands
for LLM-based services, which have largely been pursued separately but rarely
jointly. In this paper, to the best of our knowledge, we are among the first
attempts towards... |
2502.00848 | RealRAG: Retrieval-augmented Realistic Image Generation via
Self-reflective Contrastive Learning | cs.CV | Recent text-to-image generative models, e.g., Stable Diffusion V3 and Flux,
have achieved notable progress. However, these models are strongly restricted
to their limited knowledge, a.k.a., their own fixed parameters, that are
trained with closed datasets. This leads to significant hallucinations or
distortions when ... |
2502.00850 | Dual Alignment Maximin Optimization for Offline Model-based RL | cs.LG cs.AI | Offline reinforcement learning agents face significant deployment challenges
due to the synthetic-to-real distribution mismatch. While most prior research
has focused on improving the fidelity of synthetic sampling and incorporating
off-policy mechanisms, the directly integrated paradigm often fails to ensure
consist... |
2502.00854 | High-Dimensional Bayesian Optimization Using Both Random and Supervised
Embeddings | math.OC cs.LG stat.ML | Bayesian optimization (BO) is one of the most powerful strategies to solve
computationally expensive-to-evaluate blackbox optimization problems. However,
BO methods are conventionally used for optimization problems of small dimension
because of the curse of dimensionality. In this paper, a high-dimensionnal
optimizat... |
2502.00855 | Psychometric-Based Evaluation for Theorem Proving with Large Language
Models | cs.AI | Large language models (LLMs) for formal theorem proving have become a
prominent research focus. At present, the proving ability of these LLMs is
mainly evaluated through proof pass rates on datasets such as miniF2F. However,
this evaluation method overlooks the varying importance of theorems. As a
result, it fails to... |
2502.00857 | HintEval: A Comprehensive Framework for Hint Generation and Evaluation
for Questions | cs.CL cs.IR | Large Language Models (LLMs) are transforming how people find information,
and many users turn nowadays to chatbots to obtain answers to their questions.
Despite the instant access to abundant information that LLMs offer, it is still
important to promote critical thinking and problem-solving skills. Automatic
hint ge... |
2502.00858 | Learning to Plan with Personalized Preferences | cs.AI cs.HC | Effective integration of AI agents into daily life requires them to
understand and adapt to individual human preferences, particularly in
collaborative roles. Although recent studies on embodied intelligence have
advanced significantly, they typically adopt generalized approaches that
overlook personal preferences in... |
2502.00859 | FedRIR: Rethinking Information Representation in Federated Learning | cs.LG cs.DC | Mobile and Web-of-Things (WoT) devices at the network edge generate vast
amounts of data for machine learning applications, yet privacy concerns hinder
centralized model training. Federated Learning (FL) allows clients (devices) to
collaboratively train a shared model coordinated by a central server without
transfer ... |
2502.00861 | Multivariable Stochastic Newton-Based Extremum Seeking with Delays | math.OC cs.SY eess.SY | This paper presents a Newton-based stochastic extremum-seeking control method
for real-time optimization in multi-input systems with distinct input delays.
It combines predictor-based feedback and Hessian inverse estimation via
stochastic perturbations to enable delay compensation with user-defined
convergence rates.... |
2502.00865 | Predicting potentially unfair clauses in Chilean terms of services with
natural language processing | cs.CL cs.AI cs.CY cs.LG | This study addresses the growing concern of information asymmetry in consumer
contracts, exacerbated by the proliferation of online services with complex
Terms of Service that are rarely even read. Even though research on automatic
analysis methods is conducted, the problem is aggravated by the general focus
on Engli... |
2502.00869 | STAF: Sinusoidal Trainable Activation Functions for Implicit Neural
Representation | cs.CV | Implicit Neural Representations (INRs) have emerged as a powerful framework
for modeling continuous signals. The spectral bias of ReLU-based networks is a
well-established limitation, restricting their ability to capture fine-grained
details in target signals. While previous works have attempted to mitigate this
issu... |
2502.00870 | FedHPD: Heterogeneous Federated Reinforcement Learning via Policy
Distillation | cs.LG cs.AI cs.MA | Federated Reinforcement Learning (FedRL) improves sample efficiency while
preserving privacy; however, most existing studies assume homogeneous agents,
limiting its applicability in real-world scenarios. This paper investigates
FedRL in black-box settings with heterogeneous agents, where each agent employs
distinct p... |
2502.00871 | Modified Adaptive Tree-Structured Parzen Estimator for Hyperparameter
Optimization | cs.LG | In this paper, we review hyperparameter optimization methods for machine
learning models, with a particular focus on the Adaptive Tree-Structured Parzen
Estimator (ATPE) algorithm. We propose several modifications to ATPE and assess
their efficacy on a diverse set of standard benchmark functions. Experimental
results... |
2502.00873 | Language Models Use Trigonometry to Do Addition | cs.AI cs.CL cs.LG | Mathematical reasoning is an increasingly important indicator of large
language model (LLM) capabilities, yet we lack understanding of how LLMs
process even simple mathematical tasks. To address this, we reverse engineer
how three mid-sized LLMs compute addition. We first discover that numbers are
represented in thes... |
2502.00874 | Paper Copilot: The Artificial Intelligence and Machine Learning
Community Should Adopt a More Transparent and Regulated Peer Review Process | cs.DL cs.AI cs.CV cs.CY | The rapid growth of submissions to top-tier Artificial Intelligence (AI) and
Machine Learning (ML) conferences has prompted many venues to transition from
closed to open review platforms. Some have fully embraced open peer reviews,
allowing public visibility throughout the process, while others adopt hybrid
approache... |
2502.00879 | Towards Automation of Cognitive Modeling using Large Language Models | cs.LG | Computational cognitive models, which formalize theories of cognition, enable
researchers to quantify cognitive processes and arbitrate between competing
theories by fitting models to behavioral data. Traditionally, these models are
handcrafted, which requires significant domain knowledge, coding expertise, and
time ... |
2502.00882 | Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms
without Preprocessing | cs.LG cs.NA math.NA math.OC stat.ML | Due to the ever growing amounts of data leveraged for machine learning and
scientific computing, it is increasingly important to develop algorithms that
sample only a small portion of the data at a time. In the case of linear
least-squares, the randomized block Kaczmarz method (RBK) is an appealing
example of such an... |
2502.00883 | SimPER: A Minimalist Approach to Preference Alignment without
Hyperparameters | cs.LG cs.CL | Existing preference optimization objectives for language model alignment
require additional hyperparameters that must be extensively tuned to achieve
optimal performance, increasing both the complexity and time required for
fine-tuning large language models. In this paper, we propose a simple yet
effective hyperparam... |
2502.00885 | Algorithmic Stability of Stochastic Gradient Descent with Momentum under
Heavy-Tailed Noise | stat.ML cs.LG math.OC math.PR | Understanding the generalization properties of optimization algorithms under
heavy-tailed noise has gained growing attention. However, the existing
theoretical results mainly focus on stochastic gradient descent (SGD) and the
analysis of heavy-tailed optimizers beyond SGD is still missing. In this work,
we establish ... |
2502.00893 | ToddlerBot: Open-Source ML-Compatible Humanoid Platform for
Loco-Manipulation | cs.RO | Learning-based robotics research driven by data demands a new approach to
robot hardware design-one that serves as both a platform for policy execution
and a tool for embodied data collection to train policies. We introduce
ToddlerBot, a low-cost, open-source humanoid robot platform designed for
scalable policy learn... |
2502.00894 | MorphBPE: A Morpho-Aware Tokenizer Bridging Linguistic Complexity for
Efficient LLM Training Across Morphologies | cs.CL cs.AI | Tokenization is fundamental to Natural Language Processing (NLP), directly
impacting model efficiency and linguistic fidelity. While Byte Pair Encoding
(BPE) is widely used in Large Language Models (LLMs), it often disregards
morpheme boundaries, leading to suboptimal segmentation, particularly in
morphologically ric... |
2502.00896 | LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation | cs.CV | Visual prompting has gained popularity as a method for adapting pre-trained
models to specific tasks, particularly in the realm of parameter-efficient
tuning. However, existing visual prompting techniques often pad the prompt
parameters around the image, limiting the interaction between the visual
prompts and the ori... |
2502.00897 | Multi-frequency wavefield solutions for variable velocity models using
meta-learning enhanced low-rank physics-informed neural network | cs.LG physics.geo-ph | Physics-informed neural networks (PINNs) face significant challenges in
modeling multi-frequency wavefields in complex velocity models due to their
slow convergence, difficulty in representing high-frequency details, and lack
of generalization to varying frequencies and velocity scenarios. To address
these issues, we... |
2502.00899 | HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix
Decomposition for LLMs | stat.ML cs.LG | The impressive capabilities of large foundation models come at a cost of
substantial computing resources to serve them. Compressing these pre-trained
models is of practical interest as it can democratize deploying them to the
machine learning community at large by lowering the costs associated with
inference. A promi... |
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