id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.03382 | High-Fidelity Simultaneous Speech-To-Speech Translation | cs.CL cs.SD eess.AS | We introduce Hibiki, a decoder-only model for simultaneous speech
translation. Hibiki leverages a multistream language model to synchronously
process source and target speech, and jointly produces text and audio tokens to
perform speech-to-text and speech-to-speech translation. We furthermore address
the fundamental ... |
2502.03383 | Transformers and Their Roles as Time Series Foundation Models | cs.LG cs.AI | We give a comprehensive analysis of transformers as time series foundation
models, focusing on their approximation and generalization capabilities. First,
we demonstrate that there exist transformers that fit an autoregressive model
on input univariate time series via gradient descent. We then analyze MOIRAI, a
multi... |
2502.03386 | A Structured Reasoning Framework for Unbalanced Data Classification
Using Probabilistic Models | cs.LG | This paper studies a Markov network model for unbalanced data, aiming to
solve the problems of classification bias and insufficient minority class
recognition ability of traditional machine learning models in environments with
uneven class distribution. By constructing joint probability distribution and
conditional d... |
2502.03387 | LIMO: Less is More for Reasoning | cs.CL cs.AI | We present a fundamental discovery that challenges our understanding of how
complex reasoning emerges in large language models. While conventional wisdom
suggests that sophisticated reasoning tasks demand extensive training data
(>100,000 examples), we demonstrate that complex mathematical reasoning
abilities can be ... |
2502.03391 | Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise
Sufficient Reasons | cs.LG cs.LO | *Minimal sufficient reasons* represent a prevalent form of explanation - the
smallest subset of input features which, when held constant at their
corresponding values, ensure that the prediction remains unchanged. Previous
*post-hoc* methods attempt to obtain such explanations but face two main
limitations: (1) Obtai... |
2502.03393 | CAPE: Covariate-Adjusted Pre-Training for Epidemic Time Series
Forecasting | cs.LG | Accurate forecasting of epidemic infection trajectories is crucial for
safeguarding public health. However, limited data availability during emerging
outbreaks and the complex interaction between environmental factors and disease
dynamics present significant challenges for effective forecasting. In response,
we intro... |
2502.03395 | Benchmarking Time Series Forecasting Models: From Statistical Techniques
to Foundation Models in Real-World Applications | cs.LG cs.AI | Time series forecasting is essential for operational intelligence in the
hospitality industry, and particularly challenging in large-scale, distributed
systems. This study evaluates the performance of statistical, machine learning
(ML), deep learning, and foundation models in forecasting hourly sales over a
14-day ho... |
2502.03396 | Accurate AI-Driven Emergency Vehicle Location Tracking in Healthcare ITS
Digital Twin | cs.LG cs.AI cs.ET | Creating a Digital Twin (DT) for Healthcare Intelligent Transportation
Systems (HITS) is a hot research trend focusing on enhancing HITS management,
particularly in emergencies where ambulance vehicles must arrive at the crash
scene on time and track their real-time location is crucial to the medical
authorities. Des... |
2502.03397 | SPRI: Aligning Large Language Models with Context-Situated Principles | cs.CL cs.AI | Aligning Large Language Models to integrate and reflect human values,
especially for tasks that demand intricate human oversight, is arduous since it
is resource-intensive and time-consuming to depend on human expertise for
context-specific guidance. Prior work has utilized predefined sets of rules or
principles to s... |
2502.03400 | DenseReviewer: A Screening Prioritisation Tool for Systematic Review
based on Dense Retrieval | cs.IR | Screening is a time-consuming and labour-intensive yet required task for
medical systematic reviews, as tens of thousands of studies often need to be
screened. Prioritising relevant studies to be screened allows downstream
systematic review creation tasks to start earlier and save time. In previous
work, we developed... |
2502.03403 | Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin
Networks | cs.CR cs.AI | Task offloading management in 6G vehicular networks is crucial for
maintaining network efficiency, particularly as vehicles generate substantial
data. Integrating secure communication through authentication introduces
additional computational and communication overhead, significantly impacting
offloading efficiency a... |
2502.03405 | Deep Clustering via Probabilistic Ratio-Cut Optimization | cs.LG cs.CV | We propose a novel approach for optimizing the graph ratio-cut by modeling
the binary assignments as random variables. We provide an upper bound on the
expected ratio-cut, as well as an unbiased estimate of its gradient, to learn
the parameters of the assignment variables in an online setting. The clustering
resultin... |
2502.03407 | Detecting Strategic Deception Using Linear Probes | cs.LG | AI models might use deceptive strategies as part of scheming or misaligned
behaviour. Monitoring outputs alone is insufficient, since the AI might produce
seemingly benign outputs while their internal reasoning is misaligned. We thus
evaluate if linear probes can robustly detect deception by monitoring model
activati... |
2502.03409 | Verification and Synthesis Methods for High-Order Control Barrier
Functions | eess.SY cs.SY | High-order control barrier functions (HOCBFs) can be used to provide
autonomous systems with safety, though computational methods to verify and
synthesize these functions remain lacking. In this work, we address this need
by formulating SOS programs that verify and synthesize HOCBFs, such that
continued safety is alw... |
2502.03411 | Cryptocurrency Network Analysis | cs.SI cs.CY cs.NI | Cryptocurrency network analysis consists of applying the tools and methods of
social network analysis to transactional data issued from cryptocurrencies. The
main difference with most online social networks is that users do not exchange
textual content but instead value -- in systems designed mainly as
cryptocurrency... |
2502.03412 | Deep Reinforcement Learning-Based Optimization of Second-Life Battery
Utilization in Electric Vehicles Charging Stations | eess.SY cs.LG cs.SY | The rapid rise in electric vehicle (EV) adoption presents significant
challenges in managing the vast number of retired EV batteries. Research
indicates that second-life batteries (SLBs) from EVs typically retain
considerable residual capacity, offering extended utility. These batteries can
be effectively repurposed ... |
2502.03417 | From Features to Transformers: Redefining Ranking for Scalable Impact | cs.LG | We present LiGR, a large-scale ranking framework developed at LinkedIn that
brings state-of-the-art transformer-based modeling architectures into
production. We introduce a modified transformer architecture that incorporates
learned normalization and simultaneous set-wise attention to user history and
ranked items. T... |
2502.03418 | Think or Step-by-Step? UnZIPping the Black Box in Zero-Shot Prompts | cs.CL | Zero-shot prompting techniques have significantly improved the performance of
Large Language Models (LLMs). However, we lack a clear understanding of why
zero-shot prompts are so effective. For example, in the prompt "Let's think
step-by-step," is "think" or "step-by-step" more crucial to its success?
Existing interp... |
2502.03420 | Can Text-to-Image Generative Models Accurately Depict Age? A Comparative
Study on Synthetic Portrait Generation and Age Estimation | cs.CV | Text-to-image generative models have shown remarkable progress in producing
diverse and photorealistic outputs. In this paper, we present a comprehensive
analysis of their effectiveness in creating synthetic portraits that accurately
represent various demographic attributes, with a special focus on age,
nationality, ... |
2502.03421 | Investigating Corporate Social Responsibility Initiatives: Examining the
case of corporate Covid-19 response | cs.IR | In todays age of freely available information, policy makers have to take
into account a huge amount of information while making decisions affecting
relevant stakeholders. While increase in the amount of information sources and
documents increases credibility of decisions based on the corpus of available
text, it is ... |
2502.03422 | Concept Based Explanations and Class Contrasting | cs.CV | Explaining deep neural networks is challenging, due to their large size and
non-linearity. In this paper, we introduce a concept-based explanation method,
in order to explain the prediction for an individual class, as well as
contrasting any two classes, i.e. explain why the model predicts one class over
the other. W... |
2502.03424 | Prediction of the Most Fire-Sensitive Point in Building Structures with
Differentiable Agents for Thermal Simulators | cs.LG | Fire safety is a critical area of research in civil and mechanical
engineering, particularly in ensuring the structural stability of buildings
during fire events. The Most Fire-Sensitive Point (MFSP) in a structure is the
location where a fire would cause the greatest impact on structural stability.
Accurate predicti... |
2502.03426 | TruePose: Human-Parsing-guided Attention Diffusion for Full-ID
Preserving Pose Transfer | cs.CV cs.AI | Pose-Guided Person Image Synthesis (PGPIS) generates images that maintain a
subject's identity from a source image while adopting a specified target pose
(e.g., skeleton). While diffusion-based PGPIS methods effectively preserve
facial features during pose transformation, they often struggle to accurately
maintain cl... |
2502.03429 | On Fairness of Unified Multimodal Large Language Model for Image
Generation | cs.CL cs.AI | Unified multimodal large language models (U-MLLMs) have demonstrated
impressive performance in visual understanding and generation in an end-to-end
pipeline. Compared with generation-only models (e.g., Stable Diffusion),
U-MLLMs may raise new questions about bias in their outputs, which can be
affected by their unifi... |
2502.03430 | A Temporal Convolutional Network-Based Approach and a Benchmark Dataset
for Colonoscopy Video Temporal Segmentation | cs.CV eess.IV | Following recent advancements in computer-aided detection and diagnosis
systems for colonoscopy, the automated reporting of colonoscopy procedures is
set to further revolutionize clinical practice. A crucial yet underexplored
aspect in the development of these systems is the creation of computer vision
models capable... |
2502.03433 | Analyzing Political Discourse on Discord during the 2024 U.S.
Presidential Election | cs.SI | Social media networks have amplified the reach of social and political
movements, but most research focuses on mainstream platforms such as X, Reddit,
and Facebook, overlooking Discord. As a rapidly growing, community-driven
platform with optional decentralized moderation, Discord offers unique
opportunities to study... |
2502.03435 | Taking a Big Step: Large Learning Rates in Denoising Score Matching
Prevent Memorization | stat.ML cs.LG | Denoising score matching plays a pivotal role in the performance of
diffusion-based generative models. However, the empirical optimal score--the
exact solution to the denoising score matching--leads to memorization, where
generated samples replicate the training data. Yet, in practice, only a
moderate degree of memor... |
2502.03438 | BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic
Theorem Proving | cs.AI | Recent advancements in large language models (LLMs) have spurred growing
interest in automatic theorem proving using Lean4, where effective tree search
methods are crucial for navigating proof search spaces. While the existing
approaches primarily rely on value functions and Monte Carlo Tree Search
(MCTS), the potent... |
2502.03439 | Linearized Optimal Transport pyLOT Library: A Toolkit for Machine
Learning on Point Clouds | stat.ML cs.LG cs.MS stat.CO | The pyLOT library offers a Python implementation of linearized optimal
transport (LOT) techniques and methods to use in downstream tasks. The pipeline
embeds probability distributions into a Hilbert space via the Optimal Transport
maps from a fixed reference distribution, and this linearization allows
downstream task... |
2502.03444 | Masked Autoencoders Are Effective Tokenizers for Diffusion Models | cs.CV cs.AI cs.LG | Recent advances in latent diffusion models have demonstrated their
effectiveness for high-resolution image synthesis. However, the properties of
the latent space from tokenizer for better learning and generation of diffusion
models remain under-explored. Theoretically and empirically, we find that
improved generation... |
2502.03449 | Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion
Prior and Differentiable Physics | cs.CV | Recent advances in large models have significantly advanced image-to-3D
reconstruction. However, the generated models are often fused into a single
piece, limiting their applicability in downstream tasks. This paper focuses on
3D garment generation, a key area for applications like virtual try-on with
dynamic garment... |
2502.03450 | A Schema-Guided Reason-while-Retrieve framework for Reasoning on Scene
Graphs with Large-Language-Models (LLMs) | cs.LG cs.AI cs.MA cs.RO | Scene graphs have emerged as a structured and serializable environment
representation for grounded spatial reasoning with Large Language Models
(LLMs). In this work, we propose SG-RwR, a Schema-Guided Retrieve-while-Reason
framework for reasoning and planning with scene graphs. Our approach employs
two cooperative, c... |
2502.03454 | Kineto-Dynamical Planning and Accurate Execution of Minimum-Time
Maneuvers on Three-Dimensional Circuits | cs.RO | Online planning and execution of minimum-time maneuvers on three-dimensional
(3D) circuits is an open challenge in autonomous vehicle racing. In this paper,
we present an artificial race driver (ARD) to learn the vehicle dynamics, plan
and execute minimum-time maneuvers on a 3D track. ARD integrates a novel
kineto-dy... |
2502.03459 | SKI Models: Skeleton Induced Vision-Language Embeddings for
Understanding Activities of Daily Living | cs.CV | The introduction of vision-language models like CLIP has enabled the
development of foundational video models capable of generalizing to unseen
videos and human actions. However, these models are typically trained on web
videos, which often fail to capture the challenges present in Activities of
Daily Living (ADL) vi... |
2502.03460 | Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language
Model Training | cs.LG cs.AI cs.CL | Small language models (SLMs) have attracted considerable attention from both
academia and industry due to their broad range of applications in edge devices.
To obtain SLMs with strong performance, conventional approaches either
pre-train the models from scratch, which incurs substantial computational
costs, or compre... |
2502.03461 | Do Large Language Model Benchmarks Test Reliability? | cs.LG cs.CL | When deploying large language models (LLMs), it is important to ensure that
these models are not only capable, but also reliable. Many benchmarks have been
created to track LLMs' growing capabilities, however there has been no similar
focus on measuring their reliability. To understand the potential ramifications
of ... |
2502.03465 | Seeing World Dynamics in a Nutshell | cs.CV cs.AI cs.GR cs.MM | We consider the problem of efficiently representing casually captured
monocular videos in a spatially- and temporally-coherent manner. While existing
approaches predominantly rely on 2D/2.5D techniques treating videos as
collections of spatiotemporal pixels, they struggle with complex motions,
occlusions, and geometr... |
2502.03467 | Where AI Assurance Might Go Wrong: Initial lessons from engineering of
critical systems | cs.CY cs.AI cs.SE | We draw on our experience working on system and software assurance and
evaluation for systems important to society to summarise how safety engineering
is performed in traditional critical systems, such as aircraft flight control.
We analyse how this critical systems perspective might support the development
and imple... |
2502.03469 | A Capability Approach to AI Ethics | cs.CY cs.AI | We propose a conceptualization and implementation of AI ethics via the
capability approach. We aim to show that conceptualizing AI ethics through the
capability approach has two main advantages for AI ethics as a discipline.
First, it helps clarify the ethical dimension of AI tools. Second, it provides
guidance to im... |
2502.03478 | From In Silico to In Vitro: A Comprehensive Guide to Validating
Bioinformatics Findings | q-bio.GN cs.CE | The integration of bioinformatics predictions and experimental validation
plays a pivotal role in advancing biological research, from understanding
molecular mechanisms to developing therapeutic strategies. Bioinformatics tools
and methods offer powerful means for predicting gene functions, protein
interactions, and ... |
2502.03480 | Foundation for unbiased cross-validation of spatio-temporal models for
species distribution modeling | stat.AP cs.LG | Species Distribution Models (SDMs) often suffer from spatial autocorrelation
(SAC), leading to biased performance estimates. We tested cross-validation (CV)
strategies - random splits, spatial blocking with varied distances,
environmental (ENV) clustering, and a novel spatio-temporal method - under two
proposed train... |
2502.03482 | Can Domain Experts Rely on AI Appropriately? A Case Study on AI-Assisted
Prostate Cancer MRI Diagnosis | eess.IV cs.AI cs.CV cs.CY cs.HC cs.LG | Despite the growing interest in human-AI decision making, experimental
studies with domain experts remain rare, largely due to the complexity of
working with domain experts and the challenges in setting up realistic
experiments. In this work, we conduct an in-depth collaboration with
radiologists in prostate cancer d... |
2502.03484 | Dementia Classification Using Acoustic Speech and Feature Selection | eess.AS cs.LG cs.SD | Dementia is a general term for a group of syndromes that affect cognitive
functions such as memory, thinking, reasoning, and the ability to perform daily
tasks. The number of dementia patients is increasing as the population ages,
and it is estimated that over 10 million people develop dementia each year.
Dementia pr... |
2502.03487 | Artificial Intelligence and Legal Analysis: Implications for Legal
Education and the Profession | cs.CY cs.AI | This article reports the results of a study examining the ability of legal
and non-legal Large Language Models to perform legal analysis using the
Issue-Rule-Application-Conclusion framework. LLMs were tested on legal
reasoning tasks involving rule analysis and analogical reasoning. The results
show that LLMs can con... |
2502.03490 | Examining Two Hop Reasoning Through Information Content Scaling | cs.AI cs.LG | Prior work has found that transformers have an inconsistent ability to learn
to answer latent two-hop questions -- questions of the form "Who is Bob's
mother's boss?" We study why this is the case by examining how transformers'
capacity to learn datasets of two-hop questions and answers (two-hop QA) scales
with their... |
2502.03492 | Teaching Language Models to Critique via Reinforcement Learning | cs.LG cs.AI cs.CL | Teaching large language models (LLMs) to critique and refine their outputs is
crucial for building systems that can iteratively improve, yet it is
fundamentally limited by the ability to provide accurate judgments and
actionable suggestions. In this work, we study LLM critics for code generation
and propose $\texttt{... |
2502.03493 | MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from
Monocular Endoscopic Images | eess.IV cs.CV | Depth estimation from monocular endoscopic images presents significant
challenges due to the complexity of endoscopic surgery, such as irregular
shapes of human soft tissues, as well as variations in lighting conditions.
Existing methods primarily estimate the depth information from RGB images
directly, and often sur... |
2502.03499 | Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and
Multi-Task Learning | q-bio.GN cs.AI cs.LG | Large Language Models (LLMs) demonstrate remarkable generalizability across
diverse tasks, yet genomic foundation models (GFMs) still require separate
finetuning for each downstream application, creating significant overhead as
model sizes grow. Moreover, existing GFMs are constrained by rigid output
formats, limitin... |
2502.03500 | Efficient Image Restoration via Latent Consistency Flow Matching | eess.IV cs.AI stat.AP | Recent advances in generative image restoration (IR) have demonstrated
impressive results. However, these methods are hindered by their substantial
size and computational demands, rendering them unsuitable for deployment on
edge devices. This work introduces ELIR, an Efficient Latent Image Restoration
method. ELIR op... |
2502.03501 | Proxy Prompt: Endowing SAM and SAM 2 with Auto-Interactive-Prompt for
Medical Segmentation | eess.IV cs.LG | In this paper, we aim to address the unmet demand for automated prompting and
enhanced human-model interactions of SAM and SAM2 for the sake of promoting
their widespread clinical adoption. Specifically, we propose Proxy Prompt (PP),
auto-generated by leveraging non-target data with a pre-annotated mask. We
devise a ... |
2502.03502 | DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with
Video Diffusion Prior | eess.IV cs.AI cs.GR | Video super-resolution (VSR) aims to reconstruct a high-resolution (HR) video
from a low-resolution (LR) counterpart. Achieving successful VSR requires
producing realistic HR details and ensuring both spatial and temporal
consistency. To restore realistic details, diffusion-based VSR approaches have
recently been pro... |
2502.03503 | Two in context learning tasks with complex functions | stat.ML cs.AI cs.LG | We examine two in context learning (ICL) tasks with mathematical functions in
several train and test settings for transformer models. Our study generalizes
work on linear functions by showing that small transformers, even models with
attention layers only, can approximate arbitrary polynomial functions and hence
cont... |
2502.03504 | Immersion for AI: Immersive Learning with Artificial Intelligence | q-bio.NC cs.AI cs.HC | This work reflects upon what Immersion can mean from the perspective of an
Artificial Intelligence (AI). Applying the lens of immersive learning theory,
it seeks to understand whether this new perspective supports ways for AI
participation in cognitive ecologies. By treating AI as a participant rather
than a tool, it... |
2502.03505 | Enhancing Free-hand 3D Photoacoustic and Ultrasound Reconstruction using
Deep Learning | eess.IV cs.AI cs.LG | This study introduces a motion-based learning network with a global-local
self-attention module (MoGLo-Net) to enhance 3D reconstruction in handheld
photoacoustic and ultrasound (PAUS) imaging. Standard PAUS imaging is often
limited by a narrow field of view and the inability to effectively visualize
complex 3D struc... |
2502.03506 | Optimistic {\epsilon}-Greedy Exploration for Cooperative Multi-Agent
Reinforcement Learning | cs.MA cs.LG | The Centralized Training with Decentralized Execution (CTDE) paradigm is
widely used in cooperative multi-agent reinforcement learning. However, due to
the representational limitations of traditional monotonic value decomposition
methods, algorithms can underestimate optimal actions, leading policies to
suboptimal so... |
2502.03508 | Elucidation of the Concept of Consciousness from the Theory of Non-Human
Communication Agents | q-bio.NC cs.AI cs.HC | This article focuses on elucidating the concept of consciousness from a
relational and post-phenomenological theory of non-human communication agents
(ANHC). Specifically, we explore the contributions of Thomas Metzinger s Self
Model Theory, Katherine Hayles conceptualizations of non-conscious cognitive
processes cen... |
2502.03510 | Mapping and Localization Using LiDAR Fiducial Markers | cs.CV | LiDAR sensors are essential for autonomous systems, yet LiDAR fiducial
markers (LFMs) lag behind visual fiducial markers (VFMs) in adoption and
utility. Bridging this gap is vital for robotics and computer vision but
challenging due to the sparse, unstructured nature of 3D LiDAR data and
2D-focused fiducial marker de... |
2502.03511 | An Empirical Exploration of ChatGPT's Ability to Support Problem
Formulation Tasks for Mission Engineering and a Documentation of its
Performance Variability | cs.SE cs.AI cs.CL | Systems engineering (SE) is evolving with the availability of generative
artificial intelligence (AI) and the demand for a systems-of-systems
perspective, formalized under the purview of mission engineering (ME) in the US
Department of Defense. Formulating ME problems is challenging because they are
open-ended exerci... |
2502.03512 | YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing
Multi-Objective Optimization based DPO for Text-to-Image Alignment | cs.AI | Precise alignment in Text-to-Image (T2I) systems is crucial to ensure that
generated visuals not only accurately encapsulate user intents but also conform
to stringent ethical and aesthetic benchmarks. Incidents like the Google Gemini
fiasco, where misaligned outputs triggered significant public backlash,
underscore ... |
2502.03540 | Path Planning for Masked Diffusion Model Sampling | cs.LG cs.AI | In this paper, we explore how token unmasking order influences generative
quality in masked diffusion models (MDMs). We derive an expanded evidence lower
bound (ELBO) that introduces a planner to select which tokens to unmask at each
step. Our analysis reveals that alternative unmasking strategies can enhance
generat... |
2502.03544 | Gold-medalist Performance in Solving Olympiad Geometry with
AlphaGeometry2 | cs.AI cs.LG | We present AlphaGeometry2, a significantly improved version of AlphaGeometry
introduced in Trinh et al. (2024), which has now surpassed an average gold
medalist in solving Olympiad geometry problems. To achieve this, we first
extend the original AlphaGeometry language to tackle harder problems involving
movements of ... |
2502.03545 | Proportional Selection in Networks | cs.GT cs.AI cs.MA cs.SI | We address the problem of selecting $k$ representative nodes from a network,
aiming to achieve two objectives: identifying the most influential nodes and
ensuring the selection proportionally reflects the network's diversity. We
propose two approaches to accomplish this, analyze them theoretically, and
demonstrate th... |
2502.03549 | Kronecker Mask and Interpretive Prompts are Language-Action Video
Learners | cs.CV | Contrastive language-image pretraining (CLIP) has significantly advanced
image-based vision learning. A pressing topic subsequently arises: how can we
effectively adapt CLIP to the video domain? Recent studies have focused on
adjusting either the textual or visual branch of CLIP for action recognition.
However, we ar... |
2502.03550 | TD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy
Constraint | cs.LG cs.RO | Model-based reinforcement learning algorithms that combine model-based
planning and learned value/policy prior have gained significant recognition for
their high data efficiency and superior performance in continuous control.
However, we discover that existing methods that rely on standard SAC-style
policy iteration ... |
2502.03551 | Online Learning Algorithms in Hilbert Spaces with $\beta-$ and
$\phi-$Mixing Sequences | stat.ML cs.LG math.FA | In this paper, we study an online algorithm in a reproducing kernel Hilbert
spaces (RKHS) based on a class of dependent processes, called the mixing
process. For such a process, the degree of dependence is measured by various
mixing coefficients. As a representative example, we analyze a strictly
stationary Markov ch... |
2502.03552 | Can Cross Encoders Produce Useful Sentence Embeddings? | cs.CL cs.IR | Cross encoders (CEs) are trained with sentence pairs to detect relatedness.
As CEs require sentence pairs at inference, the prevailing view is that they
can only be used as re-rankers in information retrieval pipelines. Dual
encoders (DEs) are instead used to embed sentences, where sentence pairs are
encoded by two s... |
2502.03553 | Efficient Global Neural Architecture Search | cs.CV | Neural architecture search (NAS) has shown promise towards automating neural
network design for a given task, but it is computationally demanding due to
training costs associated with evaluating a large number of architectures to
find the optimal one. To speed up NAS, recent works limit the search to network
building... |
2502.03566 | CLIP Behaves like a Bag-of-Words Model Cross-modally but not Uni-modally | cs.CV cs.LG | CLIP (Contrastive Language-Image Pretraining) has become a popular choice for
various downstream tasks. However, recent studies have questioned its ability
to represent compositional concepts effectively. These works suggest that CLIP
often acts like a bag-of-words (BoW) model, interpreting images and text as
sets of... |
2502.03568 | Code Simulation as a Proxy for High-order Tasks in Large Language Models | cs.LG cs.AI | Many reasoning, planning, and problem-solving tasks share an intrinsic
algorithmic nature: correctly simulating each step is a sufficient condition to
solve them correctly. We collect pairs of naturalistic and synthetic reasoning
tasks to assess the capabilities of Large Language Models (LLM). While
naturalistic task... |
2502.03569 | Controllable Sequence Editing for Counterfactual Generation | cs.LG q-bio.GN q-bio.PE | Sequence models generate counterfactuals by modifying parts of a sequence
based on a given condition, enabling reasoning about "what if" scenarios. While
these models excel at conditional generation, they lack fine-grained control
over when and where edits occur. Existing approaches either focus on univariate
sequenc... |
2502.03571 | A Multi-Task Learning Approach to Linear Multivariate Forecasting | cs.LG cs.AI | Accurate forecasting of multivariate time series data is important in many
engineering and scientific applications. Recent state-of-the-art works ignore
the inter-relations between variates, using their model on each variate
independently. This raises several research questions related to proper
modeling of multivari... |
2502.03576 | Clone-Resistant Weights in Metric Spaces: A Framework for Handling
Redundancy Bias | cs.LG cs.GT | We are given a set of elements in a metric space. The distribution of the
elements is arbitrary, possibly adversarial. Can we weigh the elements in a way
that is resistant to such (adversarial) manipulations? This problem arises in
various contexts. For instance, the elements could represent data points,
requiring ro... |
2502.03587 | Stein Discrepancy for Unsupervised Domain Adaptation | cs.LG stat.ML | Unsupervised domain adaptation (UDA) leverages information from a labeled
source dataset to improve accuracy on a related but unlabeled target dataset. A
common approach to UDA is aligning representations from the source and target
domains by minimizing the distance between their data distributions. Previous
methods ... |
2502.03589 | HACK: Homomorphic Acceleration via Compression of the Key-Value Cache
for Disaggregated LLM Inference | cs.DC cs.LG | Disaggregated Large Language Model (LLM) inference has gained popularity as
it separates the computation-intensive prefill stage from the memory-intensive
decode stage, avoiding the prefill-decode interference and improving resource
utilization. However, transmitting Key-Value (KV) data between the two stages
can be ... |
2502.03591 | Clinically-Inspired Hierarchical Multi-Label Classification of Chest
X-rays with a Penalty-Based Loss Function | cs.CV cs.AI cs.LG | In this work, we present a novel approach to multi-label chest X-ray (CXR)
image classification that enhances clinical interpretability while maintaining
a streamlined, single-model, single-run training pipeline. Leveraging the
CheXpert dataset and VisualCheXbert-derived labels, we incorporate hierarchical
label grou... |
2502.03592 | Solar Panel Mapping via Oriented Object Detection | cs.CV | Maintaining the integrity of solar power plants is a vital component in
dealing with the current climate crisis. This process begins with analysts
creating a detailed map of a plant with the coordinates of every solar panel,
making it possible to quickly locate and mitigate potential faulty solar
panels. However, thi... |
2502.03603 | Dynamical Landauer principle: Thermodynamic criteria of transmitting
classical information | quant-ph cs.IT math-ph math.IT math.MP | Transmitting energy and information are two essential aspects of nature.
Recent findings suggest they are closely related, while a quantitative
equivalence between them is still unknown. This thus motivates us to ask: Can
information transmission tasks equal certain energy transmission tasks? We
answer this question ... |
2502.03604 | Bilevel ZOFO: Bridging Parameter-Efficient and Zeroth-Order Techniques
for Efficient LLM Fine-Tuning and Meta-Training | cs.LG | Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks
using First-Order (FO) optimizers presents significant computational
challenges. Parameter-Efficient Fine-Tuning(PEFT) methods have been proposed to
address these challenges by freezing most model parameters and training only a
small subset. Wh... |
2502.03607 | Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models | cs.RO cs.AI cs.LG | Recent advances in diffusion models hold significant potential in robotics,
enabling the generation of diverse and smooth trajectories directly from raw
representations of the environment. Despite this promise, applying diffusion
models to motion planning remains challenging due to their difficulty in
enforcing criti... |
2502.03608 | (GG) MoE vs. MLP on Tabular Data | cs.LG cs.AI | In recent years, significant efforts have been directed toward adapting
modern neural network architectures for tabular data. However, despite their
larger number of parameters and longer training and inference times, these
models often fail to consistently outperform vanilla multilayer perceptron
(MLP) neural networ... |
2502.03609 | Multivariate Conformal Prediction using Optimal Transport | stat.ML cs.LG | Conformal prediction (CP) quantifies the uncertainty of machine learning
models by constructing sets of plausible outputs. These sets are constructed by
leveraging a so-called conformity score, a quantity computed using the input
point of interest, a prediction model, and past observations. CP sets are then
obtained ... |
2502.03614 | A Novel Zero-Touch, Zero-Trust, AI/ML Enablement Framework for IoT
Network Security | cs.LG cs.AI cs.CR | The IoT facilitates a connected, intelligent, and sustainable society;
therefore, it is imperative to protect the IoT ecosystem. The IoT-based 5G and
6G will leverage the use of machine learning and artificial intelligence
(ML/AI) more to pave the way for autonomous and collaborative secure IoT
networks. Zero-touch, ... |
2502.03616 | Noncooperative Equilibrium Selection via a Trading-based Auction | cs.GT cs.MA | Noncooperative multi-agent systems often face coordination challenges due to
conflicting preferences among agents. In particular, agents acting in their own
self-interest can settle on different equilibria, leading to suboptimal
outcomes or even safety concerns. We propose an algorithm named trading auction
for conse... |
2502.03618 | The Logical Implication Steering Method for Conditional Interventions on
Transformer Generation | cs.LG | The field of mechanistic interpretability in pre-trained transformer models
has demonstrated substantial evidence supporting the ''linear representation
hypothesis'', which is the idea that high level concepts are encoded as vectors
in the space of activations of a model. Studies also show that model generation
behav... |
2502.03619 | Swarm Characteristic Classification using Robust Neural Networks with
Optimized Controllable Inputs | cs.LG | Having the ability to infer characteristics of autonomous agents would
profoundly revolutionize defense, security, and civil applications. Our
previous work was the first to demonstrate that supervised neural network time
series classification (NN TSC) could rapidly predict the tactics of swarming
autonomous agents i... |
2502.03620 | Efficient Optimal PAC Learning | cs.LG | Recent advances in the binary classification setting by Hanneke [2016b] and
Larsen [2023] have resulted in optimal PAC learners. These learners leverage,
respectively, a clever deterministic subsampling scheme and the classic
heuristic of bagging Breiman [1996]. Both optimal PAC learners use, as a
subroutine, the nat... |
2502.03621 | DynVFX: Augmenting Real Videos with Dynamic Content | cs.CV | We present a method for augmenting real-world videos with newly generated
dynamic content. Given an input video and a simple user-provided text
instruction describing the desired content, our method synthesizes dynamic
objects or complex scene effects that naturally interact with the existing
scene over time. The pos... |
2502.03622 | AdaPhish: AI-Powered Adaptive Defense and Education Resource Against
Deceptive Emails | cs.CR cs.AI | Phishing attacks remain a significant threat in the digital age, yet
organizations lack effective methods to tackle phishing attacks without leaking
sensitive information. Phish bowl initiatives are a vital part of cybersecurity
efforts against these attacks. However, traditional phish bowls require manual
anonymizat... |
2502.03623 | Large Teams Overshadow Individual Recognition | cs.SI | In an ideal world, every scientist's contribution would be fully recognized,
driving collective scientific progress. In reality, however, only a few
scientists are recognized and remembered. Sociologist Robert Merton first
described this disparity between contribution and recognition as the Matthew
Effect, where cita... |
2502.03627 | Sorting the Babble in Babel: Assessing the Performance of Language
Detection Algorithms on the OpenAlex Database | cs.CL | This project aims to compare various language classification procedures,
procedures combining various Python language detection algorithms and
metadata-based corpora extracted from manually-annotated articles sampled from
the OpenAlex database. Following an analysis of precision and recall
performance for each algori... |
2502.03628 | The Hidden Life of Tokens: Reducing Hallucination of Large
Vision-Language Models via Visual Information Steering | cs.CV cs.AI cs.LG | Large Vision-Language Models (LVLMs) can reason effectively over both textual
and visual inputs, but they tend to hallucinate syntactically coherent yet
visually ungrounded contents. In this paper, we investigate the internal
dynamics of hallucination by examining the tokens logits rankings throughout
the generation ... |
2502.03629 | REALEDIT: Reddit Edits As a Large-scale Empirical Dataset for Image
Transformations | cs.CV cs.AI cs.CL cs.LG | Existing image editing models struggle to meet real-world demands. Despite
excelling in academic benchmarks, they have yet to be widely adopted for real
user needs. Datasets that power these models use artificial edits, lacking the
scale and ecological validity necessary to address the true diversity of user
requests... |
2502.03638 | SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models | cond-mat.mtrl-sci cs.LG | Generating novel crystalline materials has potential to lead to advancements
in fields such as electronics, energy storage, and catalysis. The defining
characteristic of crystals is their symmetry, which plays a central role in
determining their physical properties. However, existing crystal generation
methods either... |
2502.03639 | Towards Physical Understanding in Video Generation: A 3D Point
Regularization Approach | cs.CV | We present a novel video generation framework that integrates 3-dimensional
geometry and dynamic awareness. To achieve this, we augment 2D videos with 3D
point trajectories and align them in pixel space. The resulting 3D-aware video
dataset, PointVid, is then used to fine-tune a latent diffusion model, enabling
it to... |
2502.03640 | Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal
Control | cs.RO cs.LG cs.MA math.OC | Control policies that can achieve high task performance and satisfy safety
constraints are desirable for any system, including multi-agent systems (MAS).
One promising technique for ensuring the safety of MAS is distributed control
barrier functions (CBF). However, it is difficult to design distributed
CBF-based poli... |
2502.03643 | Context-Preserving Gradient Modulation for Large Language Models: A
Novel Approach to Semantic Consistency in Long-Form Text Generation | cs.CL | Maintaining semantic consistency over extended text sequences remains a
fundamental challenge in long-form text generation, where conventional training
methodologies often struggle to prevent contextual drift and coherence
degradation. A novel gradient modulation approach is introduced, designed to
adjust parameter u... |
2502.03647 | Looking for the Inner Music: Probing LLMs' Understanding of Literary
Style | cs.CL cs.LG | Recent work has demonstrated that language models can be trained to identify
the author of much shorter literary passages than has been thought feasible for
traditional stylometry. We replicate these results for authorship and extend
them to a new dataset measuring novel genre. We find that LLMs are able to
distingui... |
2502.03649 | All-in-One Image Compression and Restoration | cs.CV | Visual images corrupted by various types and levels of degradations are
commonly encountered in practical image compression. However, most existing
image compression methods are tailored for clean images, therefore struggling
to achieve satisfying results on these images. Joint compression and
restoration methods typ... |
2502.03650 | Rule-based Evolving Fuzzy System for Time Series Forecasting: New
Perspectives Based on Type-2 Fuzzy Sets Measures Approach | stat.ML cs.LG | Real-world data contain uncertainty and variations that can be correlated to
external variables, known as randomness. An alternative cause of randomness is
chaos, which can be an important component of chaotic time series. One of the
existing methods to deal with this type of data is the use of the evolving
Fuzzy Sys... |
2502.03652 | The Cost of Shuffling in Private Gradient Based Optimization | cs.LG | We consider the problem of differentially private (DP) convex empirical risk
minimization (ERM). While the standard DP-SGD algorithm is theoretically
well-established, practical implementations often rely on shuffled gradient
methods that traverse the training data sequentially rather than sampling with
replacement i... |
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