id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.10268 | Optimized Strategies for Peak Shaving and BESS Efficiency Enhancement
through Cycle-Based Control and Cluster-Level Power Allocation | eess.SY cs.SY | Battery Energy Storage Systems (BESS) are essential for peak shaving,
balancing power supply and demand while enhancing grid efficiency. This study
proposes a cycle-based control strategy for charging and discharging, which
optimizes capture rate (CR), release rate (RR), and capacity utilization rate
(CUR), improving... |
2502.10273 | Probing Perceptual Constancy in Large Vision Language Models | cs.CV cs.AI | Perceptual constancy is the ability to maintain stable perceptions of objects
despite changes in sensory input, such as variations in distance, angle, or
lighting. This ability is crucial for recognizing visual information in a
dynamic world, making it essential for Vision-Language Models (VLMs). However,
whether VLM... |
2502.10277 | Artificial Intelligence to Assess Dental Findings from Panoramic
Radiographs -- A Multinational Study | cs.CV | Dental panoramic radiographs (DPRs) are widely used in clinical practice for
comprehensive oral assessment but present challenges due to overlapping
structures and time constraints in interpretation.
This study aimed to establish a solid baseline for the AI-automated
assessment of findings in DPRs by developing, ev... |
2502.10280 | Probabilistic Super-Resolution for High-Fidelity Physical System
Simulations with Uncertainty Quantification | cs.LG stat.ML | Super-resolution (SR) is a promising tool for generating high-fidelity
simulations of physical systems from low-resolution data, enabling fast and
accurate predictions in engineering applications. However, existing
deep-learning based SR methods, require large labeled datasets and lack
reliable uncertainty quantifica... |
2502.10283 | Anomaly Detection with LWE Encrypted Control | cs.CR cs.SY eess.SY | Detecting attacks using encrypted signals is challenging since encryption
hides its information content. We present a novel mechanism for anomaly
detection over Learning with Errors (LWE) encrypted signals without using
decryption, secure channels, nor complex communication schemes. Instead, the
detector exploits the... |
2502.10284 | A Hybrid Cross-Stage Coordination Pre-ranking Model for Online
Recommendation Systems | cs.IR cs.AI | Large-scale recommendation systems often adopt cascading architecture
consisting of retrieval, pre-ranking, ranking, and re-ranking stages. With
strict latency requirements, pre-ranking utilizes lightweight models to perform
a preliminary selection from massive retrieved candidates. However, recent
works focus solely... |
2502.10288 | Adversarial Mixup Unlearning | cs.LG | Machine unlearning is a critical area of research aimed at safeguarding data
privacy by enabling the removal of sensitive information from machine learning
models. One unique challenge in this field is catastrophic unlearning, where
erasing specific data from a well-trained model unintentionally removes
essential kno... |
2502.10292 | Small Loss Bounds for Online Learning Separated Function Classes: A
Gaussian Process Perspective | cs.LG stat.ML | In order to develop practical and efficient algorithms while circumventing
overly pessimistic computational lower bounds, recent work has been interested
in developing oracle-efficient algorithms in a variety of learning settings.
Two such settings of particular interest are online and differentially private
learning... |
2502.10294 | QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for
Scribble-Supervised Segmentation of Medical Images | cs.CV | The deployment of advanced deep learning models for medical image
segmentation is often constrained by the requirement for extensively annotated
datasets. Weakly-supervised learning, which allows less precise labels, has
become a promising solution to this challenge. Building on this approach, we
propose QMaxViT-Unet... |
2502.10295 | Fenchel-Young Variational Learning | cs.LG | From a variational perspective, many statistical learning criteria involve
seeking a distribution that balances empirical risk and regularization. In this
paper, we broaden this perspective by introducing a new general class of
variational methods based on Fenchel-Young (FY) losses, treated as divergences
that genera... |
2502.10297 | DeltaProduct: Increasing the Expressivity of DeltaNet Through Products
of Householders | cs.LG cs.CL cs.FL | Linear Recurrent Neural Networks (linear RNNs) have emerged as competitive
alternatives to Transformers for sequence modeling, offering efficient training
and linear-time inference. However, existing architectures face a fundamental
trade-off between expressivity and efficiency, dictated by the structure of
their sta... |
2502.10303 | Reinforcement Learning in Strategy-Based and Atari Games: A Review of
Google DeepMinds Innovations | cs.AI cs.GT | Reinforcement Learning (RL) has been widely used in many applications,
particularly in gaming, which serves as an excellent training ground for AI
models. Google DeepMind has pioneered innovations in this field, employing
reinforcement learning algorithms, including model-based, model-free, and deep
Q-network approac... |
2502.10307 | SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer
learning using Foundation Models | cs.LG cs.CV | Traditional solar forecasting models are based on several years of
site-specific historical irradiance data, often spanning five or more years,
which are unavailable for newer photovoltaic farms. As renewable energy is
highly intermittent, building accurate solar irradiance forecasting systems is
essential for effici... |
2502.10308 | LLM-Powered Preference Elicitation in Combinatorial Assignment | cs.AI cs.GT cs.LG | We study the potential of large language models (LLMs) as proxies for humans
to simplify preference elicitation (PE) in combinatorial assignment. While
traditional PE methods rely on iterative queries to capture preferences, LLMs
offer a one-shot alternative with reduced human effort. We propose a framework
for LLM p... |
2502.10310 | Object Detection and Tracking | cs.CV cs.CY | Efficient and accurate object detection is an important topic in the
development of computer vision systems. With the advent of deep learning
techniques, the accuracy of object detection has increased significantly. The
project aims to integrate a modern technique for object detection with the aim
of achieving high a... |
2502.10311 | ExplainReduce: Summarising local explanations via proxies | cs.LG cs.AI cs.HC | Most commonly used non-linear machine learning methods are closed-box models,
uninterpretable to humans. The field of explainable artificial intelligence
(XAI) aims to develop tools to examine the inner workings of these closed
boxes. An often-used model-agnostic approach to XAI involves using simple
models as local ... |
2502.10324 | Analysis and Prediction of Coverage and Channel Rank for UAV Networks in
Rural Scenarios with Foliage | eess.SY cs.SY | Unmanned aerial vehicles (UAVs) are expected to play a key role in 6G-enabled
vehicular-to-everything (V2X) communications requiring high data rates, low
latency, and reliable connectivity for mission-critical applications.
Multi-input multi-output (MIMO) technology is essential for meeting these
demands. However, UA... |
2502.10325 | Process Reward Models for LLM Agents: Practical Framework and Directions | cs.LG cs.AI | We introduce Agent Process Reward Models (AgentPRM), a simple and scalable
framework for training LLM agents to continually improve through interactions.
AgentPRM follows a lightweight actor-critic paradigm, using Monte Carlo
rollouts to compute reward targets and optimize policies. It requires minimal
modifications ... |
2502.10328 | Generalised Parallel Tempering: Flexible Replica Exchange via Flows and
Diffusions | stat.ML cs.LG | Parallel Tempering (PT) is a classical MCMC algorithm designed for leveraging
parallel computation to sample efficiently from high-dimensional, multimodal or
otherwise complex distributions via annealing. One limitation of the standard
formulation of PT is the growth of computational resources required to generate
hi... |
2502.10330 | DiOpt: Self-supervised Diffusion for Constrained Optimization | cs.LG | Recent advances in diffusion models show promising potential for
learning-based optimization by leveraging their multimodal sampling capability
to escape local optima. However, existing diffusion-based optimization
approaches, often reliant on supervised training, lacks a mechanism to ensure
strict constraint satisfa... |
2502.10331 | InfoPos: A ML-Assisted Solution Design Support Framework for Industrial
Cyber-Physical Systems | cs.LG | The variety of building blocks and algorithms incorporated in data-centric
and ML-assisted solutions is high, contributing to two challenges: selection of
most effective set and order of building blocks, as well as achieving such a
selection with minimum cost. Considering that ML-assisted solution design is
influence... |
2502.10334 | Ocular Disease Classification Using CNN with Deep Convolutional
Generative Adversarial Network | cs.CV | The Convolutional Neural Network (CNN) has shown impressive performance in
image classification because of its strong learning capabilities. However, it
demands a substantial and balanced dataset for effective training. Otherwise,
networks frequently exhibit over fitting and struggle to generalize to new
examples. Pu... |
2502.10335 | Studying number theory with deep learning: a case study with the
M\"obius and squarefree indicator functions | math.NT cs.LG | Building on work of Charton, we train small transformer models to calculate
the M\"obius function $\mu(n)$ and the squarefree indicator function
$\mu^2(n)$. The models attain nontrivial predictive power. We then iteratively
train additional models to understand how the model functions, ultimately
finding a theoretica... |
2502.10338 | Evaluating the Meta- and Object-Level Reasoning of Large Language Models
for Question Answering | cs.CL cs.AI | Large Language Models (LLMs) excel in natural language tasks but still face
challenges in Question Answering (QA) tasks requiring complex, multi-step
reasoning. We outline the types of reasoning required in some of these tasks,
and reframe them in terms of meta-level reasoning (akin to high-level strategic
reasoning ... |
2502.10339 | STAR: Spectral Truncation and Rescale for Model Merging | cs.CL cs.AI cs.LG | Model merging is an efficient way of obtaining a multi-task model from
several pretrained models without further fine-tuning, and it has gained
attention in various domains, including natural language processing (NLP).
Despite the efficiency, a key challenge in model merging is the seemingly
inevitable decrease in ta... |
2502.10341 | Organize the Web: Constructing Domains Enhances Pre-Training Data
Curation | cs.CL | Modern language models are trained on large, unstructured datasets consisting
of trillions of tokens and obtained by crawling the web. The unstructured
nature makes it difficult to reason about their contents and develop systematic
approaches to data curation. In this paper, we unpack monolithic web corpora by
develo... |
2502.10352 | Agentic Verification for Ambiguous Query Disambiguation | cs.CL | In this work, we tackle the challenge of disambiguating queries in
retrieval-augmented generation (RAG) to diverse yet answerable interpretations.
State-of-the-arts follow a Diversify-then-Verify (DtV) pipeline, where diverse
interpretations are generated by an LLM, later used as search queries to
retrieve supporting... |
2502.10353 | Assortment Optimization for Patient-Provider Matching | cs.CY cs.LG math.OC | Rising provider turnover forces healthcare administrators to frequently
rematch patients to available providers, which can be cumbersome and
labor-intensive. To reduce the burden of rematching, we study algorithms for
matching patients and providers through assortment optimization. We develop a
patient-provider match... |
2502.10354 | Dimension-free Score Matching and Time Bootstrapping for Diffusion
Models | cs.LG math.ST stat.ML stat.TH | Diffusion models generate samples by estimating the score function of the
target distribution at various noise levels. The model is trained using samples
drawn from the target distribution, progressively adding noise. In this work,
we establish the first (nearly) dimension-free sample complexity bounds for
learning t... |
2502.10357 | Learning Euler Factors of Elliptic Curves | math.NT cs.LG | We apply transformer models and feedforward neural networks to predict
Frobenius traces $a_p$ from elliptic curves given other traces $a_q$. We train
further models to predict $a_p \bmod 2$ from $a_q \bmod 2$, and cross-analysis
such as $a_p \bmod 2$ from $a_q$. Our experiments reveal that these models
achieve high a... |
2502.10359 | Proper Learnability and the Role of Unlabeled Data | cs.LG stat.ML | Proper learning refers to the setting in which learners must emit predictors
in the underlying hypothesis class $H$, and often leads to learners with simple
algorithmic forms (e.g. empirical risk minimization (ERM), structural risk
minimization (SRM)). The limitation of proper learning, however, is that there
exist p... |
2502.10361 | Enhancing Multilingual LLM Pretraining with Model-Based Data Selection | cs.CL cs.LG | Dataset curation has become a basis for strong large language model (LLM)
performance. While various rule-based filtering heuristics exist for English
and multilingual datasets, model-based filtering techniques have primarily
focused on English. To address the disparity stemming from limited research on
non-English l... |
2502.10363 | BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds | cs.RO cs.AI cs.LG | Traversing risky terrains with sparse footholds poses a significant challenge
for humanoid robots, requiring precise foot placements and stable locomotion.
Existing approaches designed for quadrupedal robots often fail to generalize to
humanoid robots due to differences in foot geometry and unstable morphology,
while... |
2502.10365 | AffinityFlow: Guided Flows for Antibody Affinity Maturation | cs.LG | Antibodies are widely used as therapeutics, but their development requires
costly affinity maturation, involving iterative mutations to enhance binding
affinity.This paper explores a sequence-only scenario for affinity maturation,
using solely antibody and antigen sequences. Recently AlphaFlow wraps AlphaFold
within ... |
2502.10367 | Decentralized State Estimation and Opacity Verification Based on
Partially Ordered Observation Sequences | eess.SY cs.SY | In this paper, we investigate state estimation and opacity verification
problems within a decentralized observation architecture. Specifically, we
consider a discrete event system whose behavior is recorded by a set of
observation sites. These sites transmit the partially ordered sequences of
observations that they r... |
2502.10373 | OWLS: Scaling Laws for Multilingual Speech Recognition and Translation
Models | cs.CL cs.AI cs.LG eess.AS | Neural scaling laws offer valuable insights for designing robust sequence
processing architectures. While these laws have been extensively characterized
in other modalities, their behavior in speech remains comparatively
underexplored. In this work, we introduce OWLS, an open-access, reproducible
suite of multilingua... |
2502.10377 | ReStyle3D: Scene-Level Appearance Transfer with Semantic Correspondences | cs.CV cs.GR | We introduce ReStyle3D, a novel framework for scene-level appearance transfer
from a single style image to a real-world scene represented by multiple views.
The method combines explicit semantic correspondences with multi-view
consistency to achieve precise and coherent stylization. Unlike conventional
stylization me... |
2502.10378 | Unknown Word Detection for English as a Second Language (ESL) Learners
Using Gaze and Pre-trained Language Models | cs.HC cs.CL | English as a Second Language (ESL) learners often encounter unknown words
that hinder their text comprehension. Automatically detecting these words as
users read can enable computing systems to provide just-in-time definitions,
synonyms, or contextual explanations, thereby helping users learn vocabulary in
a natural ... |
2502.10381 | Balancing the Scales: A Theoretical and Algorithmic Framework for
Learning from Imbalanced Data | cs.LG stat.ML | Class imbalance remains a major challenge in machine learning, especially in
multi-class problems with long-tailed distributions. Existing methods, such as
data resampling, cost-sensitive techniques, and logistic loss modifications,
though popular and often effective, lack solid theoretical foundations. As an
example... |
2502.10383 | Representation and Interpretation in Artificial and Natural Computing | cs.AI | Artificial computing machinery transforms representations through an
objective process, to be interpreted subjectively by humans, so the machine and
the interpreter are different entities, but in the putative natural computing
both processes are performed by the same agent. The method or process that
transforms a rep... |
2502.10385 | Simplifying DINO via Coding Rate Regularization | cs.CV cs.AI | DINO and DINOv2 are two model families being widely used to learn
representations from unlabeled imagery data at large scales. Their learned
representations often enable state-of-the-art performance for downstream tasks,
such as image classification and segmentation. However, they employ many
empirically motivated de... |
2502.10388 | Aspect-Oriented Summarization for Psychiatric Short-Term Readmission
Prediction | cs.CL | Recent progress in large language models (LLMs) has enabled the automated
processing of lengthy documents even without supervised training on a
task-specific dataset. Yet, their zero-shot performance in complex tasks as
opposed to straightforward information extraction tasks remains suboptimal. One
feasible approach ... |
2502.10389 | Region-Adaptive Sampling for Diffusion Transformers | cs.CV cs.AI | Diffusion models (DMs) have become the leading choice for generative tasks
across diverse domains. However, their reliance on multiple sequential forward
passes significantly limits real-time performance. Previous acceleration
methods have primarily focused on reducing the number of sampling steps or
reusing intermed... |
2502.10390 | (How) Can Transformers Predict Pseudo-Random Numbers? | cs.LG cond-mat.dis-nn cs.CR stat.ML | Transformers excel at discovering patterns in sequential data, yet their
fundamental limitations and learning mechanisms remain crucial topics of
investigation. In this paper, we study the ability of Transformers to learn
pseudo-random number sequences from linear congruential generators (LCGs),
defined by the recurr... |
2502.10391 | MM-RLHF: The Next Step Forward in Multimodal LLM Alignment | cs.CL cs.CV | Despite notable advancements in Multimodal Large Language Models (MLLMs),
most state-of-the-art models have not undergone thorough alignment with human
preferences. This gap exists because current alignment research has primarily
achieved progress in specific areas (e.g., hallucination reduction), while the
broader q... |
2502.10392 | Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding | cs.CV cs.LG | In this paper, we propose an efficient multi-level convolution architecture
for 3D visual grounding. Conventional methods are difficult to meet the
requirements of real-time inference due to the two-stage or point-based
architecture. Inspired by the success of multi-level fully sparse convolutional
architecture in 3D... |
2502.10394 | A Coordination-based Approach for Focused Learning in Knowledge-Based
Systems | cs.AI cs.CL | Recent progress in Learning by Reading and Machine Reading systems has
significantly increased the capacity of knowledge-based systems to learn new
facts. In this work, we discuss the problem of selecting a set of learning
requests for these knowledge-based systems which would lead to maximum Q/A
performance. To unde... |
2502.10395 | An Integrated Platform for Studying Learning with Intelligent Tutoring
Systems: CTAT+TutorShop | cs.CY cs.AI cs.HC | Intelligent tutoring systems (ITSs) are effective in helping students learn;
further research could make them even more effective. Particularly desirable is
research into how students learn with these systems, how these systems best
support student learning, and what learning sciences principles are key in
ITSs. CTAT... |
2502.10396 | DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing | cs.CY cs.AI cs.LG | Knowledge Tracing (KT) predicts future performance by modeling students'
historical interactions, and understanding students' affective states can
enhance the effectiveness of KT, thereby improving the quality of education.
Although traditional KT values students' cognition and learning behaviors,
efficient evaluatio... |
2502.10398 | Practical Application and Limitations of AI Certification Catalogues in
the Light of the AI Act | cs.CY cs.AI cs.LG | In this work-in-progress, we investigate the certification of AI systems,
focusing on the practical application and limitations of existing certification
catalogues in the light of the AI Act by attempting to certify a publicly
available AI system. We aim to evaluate how well current approaches work to
effectively ce... |
2502.10399 | Data Stewardship Decoded: Mapping Its Diverse Manifestations and
Emerging Relevance at a time of AI | cs.CY cs.AI cs.DB | Data stewardship has become a critical component of modern data governance,
especially with the growing use of artificial intelligence (AI). Despite its
increasing importance, the concept of data stewardship remains ambiguous and
varies in its application. This paper explores four distinct manifestations of
data stew... |
2502.10401 | You Can't Get There From Here: Redefining Information Science to address
our sociotechnical futures | cs.CY cs.AI cs.HC | Current definitions of Information Science are inadequate to comprehensively
describe the nature of its field of study and for addressing the problems that
are arising from intelligent technologies. The ubiquitous rise of artificial
intelligence applications and their impact on society demands the field of
Informatio... |
2502.10403 | Implementing agile healthcare frame works in the context of low income
countries: Proposed Framework and Review | cs.ET cs.CY cs.IR | Agile healthcare frameworks, derived from methodologies in IT and
manufacturing, offer transformative potential for low-income regions. This
study explores Agile integration in resource-constrained environments, focusing
on Ghana. Key benefits include adaptability, iterative planning, and
stakeholder collaboration to... |
2502.10406 | FishBargain: An LLM-Empowered Bargaining Agent for Online Fleamarket
Platform Sellers | cs.CY cs.AI | Different from traditional Business-to-Consumer e-commerce platforms~(e.g.,
Amazon), online fleamarket platforms~(e.g., Craigslist) mainly focus on
individual sellers who are lack of time investment and business proficiency.
Individual sellers often struggle with the bargaining process and thus the deal
is unaccompli... |
2502.10407 | Addressing Bias in Generative AI: Challenges and Research Opportunities
in Information Management | cs.CY cs.AI cs.HC | Generative AI technologies, particularly Large Language Models (LLMs), have
transformed information management systems but introduced substantial biases
that can compromise their effectiveness in informing business decision-making.
This challenge presents information management scholars with a unique
opportunity to a... |
2502.10408 | Knowledge Tracing in Programming Education Integrating Students'
Questions | cs.CY cs.AI cs.SE | Knowledge tracing (KT) in programming education presents unique challenges
due to the complexity of coding tasks and the diverse methods students use to
solve problems. Although students' questions often contain valuable signals
about their understanding and misconceptions, traditional KT models often
neglect to inco... |
2502.10409 | Data Science Students Perspectives on Learning Analytics: An Application
of Human-Led and LLM Content Analysis | cs.CY cs.AI cs.ET stat.AP | Objective This study is part of a series of initiatives at a UK university
designed to cultivate a deep understanding of students' perspectives on
analytics that resonate with their unique learning needs. It explores
collaborative data processing undertaken by postgraduate students who examined
an Open University Lea... |
2502.10410 | Auto-Evaluation: A Critical Measure in Driving Improvements in Quality
and Safety of AI-Generated Lesson Resources | cs.CY cs.AI | As a publicly funded body in the UK, Oak National Academy is in a unique
position to innovate within this field as we have a comprehensive curriculum of
approximately 13,000 open education resources (OER) for all National Curriculum
subjects, designed and quality-assured by expert, human teachers. This has
provided t... |
2502.10411 | TrueReason: An Exemplar Personalised Learning System Integrating
Reasoning with Foundational Models | cs.CY cs.AI cs.CL cs.IR cs.MA | Personalised education is one of the domains that can greatly benefit from
the most recent advances in Artificial Intelligence (AI) and Large Language
Models (LLM). However, it is also one of the most challenging applications due
to the cognitive complexity of teaching effectively while personalising the
learning exp... |
2502.10412 | Identifying relevant indicators for monitoring a National Artificial
Intelligence Strategy | cs.CY cs.AI | How can a National Artificial Intelligence Strategy be effectively monitored?
To address this question, we propose a methodology consisting of two key
components. First, it involves identifying relevant indicators within national
AI strategies. Second, it assesses the alignment between these indicators and
the strate... |
2502.10413 | Machine Learning-Driven Convergence Analysis in Multijurisdictional
Compliance Using BERT and K-Means Clustering | cs.CY cs.AI cs.CE cs.CL cs.LG | Digital data continues to grow, there has been a shift towards using
effective regulatory mechanisms to safeguard personal information. The CCPA of
California and the General Data Protection Regulation (GDPR) of the European
Union are two of the most important privacy laws. The regulation is intended to
safeguard con... |
2502.10414 | A Neural Network Training Method Based on Neuron Connection Coefficient
Adjustments | cs.NE cs.LG | In previous studies, we introduced a neural network framework based on
symmetric differential equations, along with one of its training methods. In
this article, we present another training approach for this neural network.
This method leverages backward signal propagation and eliminates reliance on
the traditional c... |
2502.10417 | Evolutionary Power-Aware Routing in VANETs using Monte-Carlo Simulation | cs.NE cs.AI cs.NI | This work addresses the reduction of power consumption of the AODV routing
protocol in vehicular networks as an optimization problem. Nowadays, network
designers focus on energy-aware communication protocols, specially to deploy
wireless networks. Here, we introduce an automatic method to search for
energy-efficient ... |
2502.10418 | A Novel Multi-Objective Evolutionary Algorithm for Counterfactual
Generation | cs.NE cs.LG | Machine learning algorithms that learn black-box predictive models (which
cannot be directly interpreted) are increasingly used to make predictions
affecting the lives of people. It is important that users understand the
predictions of such models, particularly when the model outputs a negative
prediction for the use... |
2502.10419 | A Hybrid Swarm Intelligence Approach for Optimizing Multimodal Large
Language Models Deployment in Edge-Cloud-based Federated Learning
Environments | cs.NE cs.AI cs.LG | The combination of Federated Learning (FL), Multimodal Large Language Models
(MLLMs), and edge-cloud computing enables distributed and real-time data
processing while preserving privacy across edge devices and cloud
infrastructure. However, the deployment of MLLMs in FL environments with
resource-constrained edge dev... |
2502.10420 | Position: Stop Acting Like Language Model Agents Are Normal Agents | cs.AI cs.CL | Language Model Agents (LMAs) are increasingly treated as capable of
autonomously navigating interactions with humans and tools. Their design and
deployment tends to presume they are normal agents capable of sustaining
coherent goals, adapting across contexts and acting with a measure of
intentionality. These assumpti... |
2502.10421 | DRiVE: Dynamic Recognition in VEhicles using snnTorch | cs.NE cs.AI cs.CV cs.LG | Spiking Neural Networks (SNNs) mimic biological brain activity, processing
data efficiently through an event-driven design, wherein the neurons activate
only when inputs exceed specific thresholds. Their ability to track voltage
changes over time via membrane potential dynamics helps retain temporal
information. This... |
2502.10422 | DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking
Neural Networks | cs.NE cs.AI | Spiking Neural Networks (SNNs) are valued for their ability to process
spatio-temporal information efficiently, offering biological plausibility, low
energy consumption, and compatibility with neuromorphic hardware. However, the
commonly used Leaky Integrate-and-Fire (LIF) model overlooks neuron
heterogeneity and ind... |
2502.10423 | Spiking Neural Network Feature Discrimination Boosts Modality Fusion | cs.NE cs.CV cs.LG eess.IV | Feature discrimination is a crucial aspect of neural network design, as it
directly impacts the network's ability to distinguish between classes and
generalize across diverse datasets. The accomplishment of achieving
high-quality feature representations ensures high intra-class separability and
poses one of the most ... |
2502.10424 | QuantSpec: Self-Speculative Decoding with Hierarchical Quantized KV
Cache | cs.LG cs.AI | Large Language Models (LLMs) are increasingly being deployed on edge devices
for long-context settings, creating a growing need for fast and efficient
long-context inference. In these scenarios, the Key-Value (KV) cache is the
primary bottleneck in terms of both GPU memory and latency, as the full KV
cache must be lo... |
2502.10425 | Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive
Learning | q-bio.NC cs.AI cs.NE | The Platonic Representation Hypothesis suggests a universal,
modality-independent reality representation behind different data modalities.
Inspired by this, we view each neuron as a system and detect its multi-segment
activity data under various peripheral conditions. We assume there's a
time-invariant representation... |
2502.10428 | Dynamic Chain-of-Thought: Towards Adaptive Deep Reasoning | cs.AI cs.LG | To reduce the cost and consumption of computing resources caused by
computational redundancy and delayed reward assignment in long CoT, this
research proposes the dynamic chain-of-thought (D-CoT) with adaptive reasoning
time and steps. The researcher used simulation experiment to simulate the
integration of D-CoT thr... |
2502.10429 | Real Time Control of Tandem-Wing Experimental Platform Using Concerto
Reinforcement Learning | cs.LG cs.AI cs.RO cs.SY eess.SY | This paper introduces the CRL2RT algorithm, an advanced reinforcement
learning method aimed at improving the real-time control performance of the
Direct-Drive Tandem-Wing Experimental Platform (DDTWEP). Inspired by dragonfly
flight, DDTWEP's tandem wing structure causes nonlinear and unsteady
aerodynamic interactions... |
2502.10431 | Leveraging Constraint Violation Signals For Action-Constrained
Reinforcement Learning | cs.LG cs.AI | In many RL applications, ensuring an agent's actions adhere to constraints is
crucial for safety. Most previous methods in Action-Constrained Reinforcement
Learning (ACRL) employ a projection layer after the policy network to correct
the action. However projection-based methods suffer from issues like the zero
gradie... |
2502.10432 | A Case Study on Virtual and Physical I/O Throughputs | cs.DC cs.DB | Input/Output (I/O) performance is one of the key areas that need to be
carefully examined to better support IT services. With the rapid development
and deployment of virtualization technology, many essential business
applications have been migrated to the virtualized platform due to reduced cost
and improved agility.... |
2502.10433 | Neural Genetic Search in Discrete Spaces | cs.NE cs.LG | Effective search methods are crucial for improving the performance of deep
generative models at test time. In this paper, we introduce a novel test-time
search method, Neural Genetic Search (NGS), which incorporates the evolutionary
mechanism of genetic algorithms into the generation procedure of deep models.
The cor... |
2502.10434 | Agency in Artificial Intelligence Systems | cs.AI cs.CY | There is a general concern that present developments in artificial
intelligence (AI) research will lead to sentient AI systems, and these may pose
an existential threat to humanity. But why cannot sentient AI systems benefit
humanity instead? This paper endeavours to put this question in a tractable
manner. I ask whe... |
2502.10435 | RAMer: Reconstruction-based Adversarial Model for Multi-party
Multi-modal Multi-label Emotion Recognition | cs.CV cs.AI | Conventional multi-modal multi-label emotion recognition (MMER) from videos
typically assumes full availability of visual, textual, and acoustic
modalities. However, real-world multi-party settings often violate this
assumption, as non-speakers frequently lack acoustic and textual inputs,
leading to a significant deg... |
2502.10436 | MERGE$^3$: Efficient Evolutionary Merging on Consumer-grade GPUs | cs.NE cs.AI cs.LG | Evolutionary model merging enables the creation of high-performing multi-task
models but remains computationally prohibitive for consumer hardware. We
introduce MERGE$^3$, an efficient framework that makes evolutionary merging
feasible on a single GPU by reducing fitness computation costs 50$\times$ while
preserving ... |
2502.10438 | Injecting Universal Jailbreak Backdoors into LLMs in Minutes | cs.CR cs.AI cs.LG | Jailbreak backdoor attacks on LLMs have garnered attention for their
effectiveness and stealth. However, existing methods rely on the crafting of
poisoned datasets and the time-consuming process of fine-tuning. In this work,
we propose JailbreakEdit, a novel jailbreak backdoor injection method that
exploits model edi... |
2502.10439 | Crypto Miner Attack: GPU Remote Code Execution Attacks | cs.CR cs.AI cs.LG | Remote Code Execution (RCE) exploits pose a significant threat to AI and ML
systems, particularly in GPU-accelerated environments where the computational
power of GPUs can be misused for malicious purposes. This paper focuses on RCE
attacks leveraging deserialization vulnerabilities and custom layers, such as
TensorF... |
2502.10440 | Towards Copyright Protection for Knowledge Bases of Retrieval-augmented
Language Models via Ownership Verification with Reasoning | cs.CR cs.AI cs.CL cs.IR cs.LG | Large language models (LLMs) are increasingly integrated into real-world
applications through retrieval-augmented generation (RAG) mechanisms to
supplement their responses with up-to-date and domain-specific knowledge.
However, the valuable and often proprietary nature of the knowledge bases used
in RAG introduces th... |
2502.10441 | AI Alignment at Your Discretion | cs.AI cs.CY cs.LG | In AI alignment, extensive latitude must be granted to annotators, either
human or algorithmic, to judge which model outputs are `better' or `safer.' We
refer to this latitude as alignment discretion. Such discretion remains largely
unexamined, posing two risks: (i) annotators may use their power of discretion
arbitr... |
2502.10442 | Analysis of Overparameterization in Continual Learning under a Linear
Model | cs.LG cs.AI stat.ML | Autonomous machine learning systems that learn many tasks in sequence are
prone to the catastrophic forgetting problem. Mathematical theory is needed in
order to understand the extent of forgetting during continual learning. As a
foundational step towards this goal, we study continual learning and
catastrophic forget... |
2502.10443 | One Class Restricted Kernel Machines | cs.LG | Restricted kernel machines (RKMs) have demonstrated a significant impact in
enhancing generalization ability in the field of machine learning. Recent
studies have introduced various methods within the RKM framework, combining
kernel functions with the least squares support vector machine (LSSVM) in a
manner similar t... |
2502.10444 | A Survey of Representation Learning, Optimization Strategies, and
Applications for Omnidirectional Vision | cs.CV | Omnidirectional image (ODI) data is captured with a field-of-view of 360x180,
which is much wider than the pinhole cameras and captures richer surrounding
environment details than the conventional perspective images. In recent years,
the availability of customer-level 360 cameras has made omnidirectional vision
more ... |
2502.10446 | Evaluating and Explaining Earthquake-Induced Liquefaction Potential
through Multi-Modal Transformers | cs.LG physics.geo-ph | This study presents an explainable parallel transformer architecture for soil
liquefaction prediction that integrates three distinct data streams: spectral
seismic encoding, soil stratigraphy tokenization, and site-specific features.
The architecture processes data from 165 case histories across 11 major
earthquakes,... |
2502.10447 | MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech
Recognition | eess.AS cs.CL cs.LG | Audio-visual speech recognition (AVSR) has become critical for enhancing
speech recognition in noisy environments by integrating both auditory and
visual modalities. However, existing AVSR systems struggle to scale up without
compromising computational efficiency. In this study, we introduce MoHAVE
(Mixture of Hierar... |
2502.10450 | Trustworthy AI on Safety, Bias, and Privacy: A Survey | cs.CR cs.AI cs.CL cs.LG | The capabilities of artificial intelligence systems have been advancing to a
great extent, but these systems still struggle with failure modes,
vulnerabilities, and biases. In this paper, we study the current state of the
field, and present promising insights and perspectives regarding concerns that
challenge the tru... |
2502.10451 | FlexControl: Computation-Aware ControlNet with Differentiable Router for
Text-to-Image Generation | cs.LG cs.GR | ControlNet offers a powerful way to guide diffusion-based generative models,
yet most implementations rely on ad-hoc heuristics to choose which network
blocks to control-an approach that varies unpredictably with different tasks.
To address this gap, we propose FlexControl, a novel framework that copies all
diffusion... |
2502.10452 | Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks
with a New Dataset | cs.LG eess.IV | This paper addresses the vulnerability of deep-learning models designed for
rain, snow, and haze removal. Despite enhancing image quality in adverse
weather, these models are susceptible to adversarial attacks that compromise
their effectiveness. Traditional defenses such as adversarial training and
model distillatio... |
2502.10453 | Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An
LLM-based Approach | cs.CR cs.AI cs.CL cs.DB cs.LG | Attribution tags form the foundation of modern cryptoasset forensics.
However, inconsistent or incorrect tags can mislead investigations and even
result in false accusations. To address this issue, we propose a novel
computational method based on Large Language Models (LLMs) to link attribution
tags with well-defined... |
2502.10454 | One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual
Reasoning in Mathematical LLMs | cs.LG cs.AI cs.CL | Leveraging mathematical Large Language Models (LLMs) for proof generation is
a fundamental topic in LLMs research. We argue that the ability of current LLMs
to prove statements largely depends on whether they have encountered the
relevant proof process during training. This reliance limits their deeper
understanding ... |
2502.10455 | E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal
Out-of-Context Misinformation Detection | cs.LG cs.MM | Recent studies in Large Vision-Language Models (LVLMs) have demonstrated
impressive advancements in multimodal Out-of-Context (OOC) misinformation
detection, discerning whether an authentic image is wrongly used in a claim.
Despite their success, the textual evidence of authentic images retrieved from
the inverse sea... |
2502.10456 | Deep Reinforcement Learning-Based User Scheduling for Collaborative
Perception | cs.LG cs.RO | Stand-alone perception systems in autonomous driving suffer from limited
sensing ranges and occlusions at extended distances, potentially resulting in
catastrophic outcomes. To address this issue, collaborative perception is
envisioned to improve perceptual accuracy by using vehicle-to-everything (V2X)
communication ... |
2502.10458 | I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning
in Diffusion Models | cs.LG cs.AI | This paper presents ThinkDiff, a novel alignment paradigm that empowers
text-to-image diffusion models with multimodal in-context understanding and
reasoning capabilities by integrating the strengths of vision-language models
(VLMs). Existing multimodal diffusion finetuning methods largely focus on
pixel-level recons... |
2502.10459 | LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural
Architecture Search | cs.LG cs.AI | Graph Neural Architecture Search (GNAS) facilitates the automatic design of
Graph Neural Networks (GNNs) tailored to specific downstream graph learning
tasks. However, existing GNAS approaches often require manual adaptation to new
graph search spaces, necessitating substantial code optimization and
domain-specific k... |
2502.10460 | SenDaL: An Effective and Efficient Calibration Framework of Low-Cost
Sensors for Daily Life | cs.LG | The collection of accurate and noise-free data is a crucial part of Internet
of Things (IoT)-controlled environments. However, the data collected from
various sensors in daily life often suffer from inaccuracies. Additionally,
IoT-controlled devices with low-cost sensors lack sufficient hardware resources
to employ c... |
2502.10461 | Performance of energy harvesters with parameter mismatch | eess.SY cs.SY | This study explores the impact of parameter mismatch on the stability of
cross-well motion in energy harvesters, using a basin stability metric. Energy
harvesters, essential for converting ambient energy into electricity,
increasingly incorporate multi-well systems to enhance efficiency. However,
these systems are se... |
2502.10463 | From Layers to States: A State Space Model Perspective to Deep Neural
Network Layer Dynamics | cs.LG cs.AI cs.NI | The depth of neural networks is a critical factor for their capability, with
deeper models often demonstrating superior performance. Motivated by this,
significant efforts have been made to enhance layer aggregation - reusing
information from previous layers to better extract features at the current
layer, to improve... |
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