id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.10012 | Dream to Drive: Model-Based Vehicle Control Using Analytic World Models | cs.AI cs.RO | Differentiable simulators have recently shown great promise for training
autonomous vehicle controllers. Being able to backpropagate through them, they
can be placed into an end-to-end training loop where their known dynamics turn
into useful priors for the policy to learn, removing the typical black box
assumption o... |
2502.10013 | Probabilistic Lexical Manifold Construction in Large Language Models via
Hierarchical Vector Field Interpolation | cs.CL | Hierarchical vector field interpolation introduces a structured probabilistic
framework for lexical representation, ensuring that word embeddings transition
smoothly across a continuous manifold rather than being constrained to discrete
token mappings. The proposed methodology constructs a probabilistic function
spac... |
2502.10014 | Recovering nonlinear dynamics from non-uniform observations: A
physics-based identification approach with practical case studies | eess.SY cs.SY | Uniform and smooth data collection is often infeasible in real-world
scenarios. In this paper, we propose an identification framework to effectively
handle the so-called non-uniform observations, i.e., data scenarios that
include missing measurements, multiple runs, or aggregated observations. The
goal is to provide ... |
2502.10019 | A Differential Equation Approach to the Most-Informative Boolean
Function Conjecture | cs.IT math.IT | We study the most-informative Boolean function conjecture using a
differential equation approach. This leads to a formulation of a functional
inequality on finite-dimensional random variables. We also develop a similar
inequality in the case of the Hellinger conjecture. Finally, we conjecture a
specific finite dimens... |
2502.10020 | Improved Online Confidence Bounds for Multinomial Logistic Bandits | stat.ML cs.LG | In this paper, we propose an improved online confidence bound for multinomial
logistic (MNL) models and apply this result to MNL bandits, achieving
variance-dependent optimal regret. Recently, Lee & Oh (2024) established an
online confidence bound for MNL models and achieved nearly minimax-optimal
regret in MNL bandi... |
2502.10027 | Heterogeneous Resource Allocation with Multi-task Learning for Wireless
Networks | cs.LG | The optimal solution to an optimization problem depends on the problem's
objective function, constraints, and size. While deep neural networks (DNNs)
have proven effective in solving optimization problems, changes in the
problem's size, objectives, or constraints often require adjustments to the DNN
architecture to m... |
2502.10028 | ManiTrend: Bridging Future Generation and Action Prediction with 3D Flow
for Robotic Manipulation | cs.CV cs.RO | Language-conditioned manipulation is a vital but challenging robotic task due
to the high-level abstraction of language. To address this, researchers have
sought improved goal representations derived from natural language. In this
paper, we highlight 3D flow - representing the motion trend of 3D particles
within a sc... |
2502.10038 | POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI
Representation Learning | cs.AI | POI representation learning plays a crucial role in handling tasks related to
user mobility data. Recent studies have shown that enriching POI
representations with multimodal information can significantly enhance their
task performance. Previously, the textual information incorporated into POI
representations typical... |
2502.10040 | Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation | cs.RO | Recently, Vision-Language-Action models (VLA) have advanced robot imitation
learning, but high data collection costs and limited demonstrations hinder
generalization and current imitation learning methods struggle in
out-of-distribution scenarios, especially for long-horizon tasks. A key
challenge is how to mitigate ... |
2502.10042 | Scaling Law Tradeoff Between Throughput and Sensing Distance in Large
ISAC Networks | cs.IT math.IT | In this paper, we investigate the fundamental tradeoff between communication
and sensing performance of \emph{ad hoc} integrated sensing and communication
(ISAC) wireless networks. Specifically, we consider that $n$ nodes are randomly
located in an extended network with area $n$ and transmit ISAC signals. Under
the p... |
2502.10044 | Unsupervised Entity Alignment Based on Personalized Discriminative
Rooted Tree | cs.AI | Entity Alignment (EA) is to link potential equivalent entities across
different knowledge graphs (KGs). Most existing EA methods are supervised as
they require the supervision of seed alignments, i.e., manually specified
aligned entity pairs. Very recently, several EA studies have made some attempts
to get rid of see... |
2502.10046 | ViRAC: A Vision-Reasoning Agent Head Movement Control Framework in
Arbitrary Virtual Environments | cs.GR cs.CV | Creating lifelike virtual agents capable of interacting with their
environments is a longstanding goal in computer graphics. This paper addresses
the challenge of generating natural head rotations, a critical aspect of
believable agent behavior for visual information gathering and dynamic
responses to environmental c... |
2502.10047 | Janus: Collaborative Vision Transformer Under Dynamic Network
Environment | cs.DC cs.AI | Vision Transformers (ViTs) have outperformed traditional Convolutional Neural
Network architectures and achieved state-of-the-art results in various computer
vision tasks. Since ViTs are computationally expensive, the models either have
to be pruned to run on resource-limited edge devices only or have to be
executed ... |
2502.10050 | A Survey on LLM-powered Agents for Recommender Systems | cs.IR cs.AI | Recommender systems are essential components of many online platforms, yet
traditional approaches still struggle with understanding complex user
preferences and providing explainable recommendations. The emergence of Large
Language Model (LLM)-powered agents offers a promising approach by enabling
natural language in... |
2502.10051 | ORI: O Routing Intelligence | cs.CL | Single large language models (LLMs) often fall short when faced with the
ever-growing range of tasks, making a single-model approach insufficient. We
address this challenge by proposing ORI (O Routing Intelligence), a dynamic
framework that leverages a set of LLMs. By intelligently routing incoming
queries to the mos... |
2502.10054 | Towards Polyp Counting In Full-Procedure Colonoscopy Videos | cs.CV | Automated colonoscopy reporting holds great potential for enhancing quality
control and improving cost-effectiveness of colonoscopy procedures. A major
challenge lies in the automated identification, tracking, and re-association
(ReID) of polyps tracklets across full-procedure colonoscopy videos. This is
essential fo... |
2502.10057 | A Generalized Modeling Approach to Liquid-driven Ballooning Membranes | cs.RO | Soft robotics is advancing the use of flexible materials for adaptable
robotic systems. Membrane-actuated soft robots address the limitations of
traditional soft robots by using pressurized, extensible membranes to achieve
stable, large deformations, yet control and state estimation remain challenging
due to their co... |
2502.10058 | MTLM: an Innovative Language Model Training Paradigm for ASR | cs.CL eess.AS | Pre-training Transformer-based language models (LMs) on a large amount of
text has proven crucial for improving automatic speech recognition (ASR)
performance. Generally, traditional LMs are unidirectional and unable to access
the context on the right. This paper proposes a method for training LMs that
enable traditi... |
2502.10059 | RealCam-I2V: Real-World Image-to-Video Generation with Interactive
Complex Camera Control | cs.CV | Recent advancements in camera-trajectory-guided image-to-video generation
offer higher precision and better support for complex camera control compared
to text-based approaches. However, they also introduce significant usability
challenges, as users often struggle to provide precise camera parameters when
working wit... |
2502.10060 | DiSciPLE: Learning Interpretable Programs for Scientific Visual
Discovery | cs.CV cs.LG | Visual data is used in numerous different scientific workflows ranging from
remote sensing to ecology. As the amount of observation data increases, the
challenge is not just to make accurate predictions but also to understand the
underlying mechanisms for those predictions. Good interpretation is important
in scienti... |
2502.10061 | Annotating Compositionality Scores for Irish Noun Compounds is Hard Work | cs.CL | Noun compounds constitute a challenging construction for NLP applications,
given their variability in idiomaticity and interpretation. In this paper, we
present an analysis of compound nouns identified in Irish text of varied
domains by expert annotators, focusing on compositionality as a key feature,
but also domain... |
2502.10062 | Adaptive Bi-Level Multi-Robot Task Allocation and Learning under
Uncertainty with Temporal Logic Constraints | cs.RO cs.AI cs.FL | This work addresses the problem of multi-robot coordination under unknown
robot transition models, ensuring that tasks specified by Time Window Temporal
Logic are satisfied with user-defined probability thresholds. We present a
bi-level framework that integrates (i) high-level task allocation, where tasks
are assigne... |
2502.10063 | Strassen Multisystolic Array Hardware Architectures | cs.AR cs.AI cs.PF | While Strassen's matrix multiplication algorithm reduces the complexity of
naive matrix multiplication, general-purpose hardware is not suitable for
achieving the algorithm's promised theoretical speedups. This leaves the
question of if it could be better exploited in custom hardware architectures
designed specifical... |
2502.10064 | Hands-off Image Editing: Language-guided Editing without any
Task-specific Labeling, Masking or even Training | cs.CL cs.CV | Instruction-guided image editing consists in taking an image and an
instruction and deliverring that image altered according to that instruction.
State-of-the-art approaches to this task suffer from the typical scaling up and
domain adaptation hindrances related to supervision as they eventually resort
to some kind o... |
2502.10070 | Topological Neural Networks over the Air | cs.IT cs.LG math.IT | Topological neural networks (TNNs) are information processing architectures
that model representations from data lying over topological spaces (e.g.,
simplicial or cell complexes) and allow for decentralized implementation
through localized communications over different neighborhoods. Existing TNN
architectures have ... |
2502.10072 | LifeSaver: Predictive Load Limit Estimation for Transport Vehicles in
Hilly Areas | eess.SY cs.SY | The transportation of essential goods in mountainous regions faces severe
logistical challenges and frequent disruptions. To mitigate these difficulties,
transport companies often overload trucks, which, though cost-saving,
significantly heightens the risk of accidents and mechanical failures. This
paper presents the... |
2502.10076 | Classification of Temporal Graphs using Persistent Homology | cs.LG cs.CG math.AT | Temporal graphs effectively model dynamic systems by representing
interactions as timestamped edges. However, analytical tools for temporal
graphs are limited compared to static graphs. We propose a novel method for
analyzing temporal graphs using Persistent Homology. Our approach leverages
$\delta$-temporal motifs (... |
2502.10077 | Towards Empowerment Gain through Causal Structure Learning in
Model-Based RL | cs.AI cs.LG | In Model-Based Reinforcement Learning (MBRL), incorporating causal structures
into dynamics models provides agents with a structured understanding of the
environments, enabling efficient decision. Empowerment as an intrinsic
motivation enhances the ability of agents to actively control their
environments by maximizin... |
2502.10080 | Coordinated control of multiple autonomous surface vehicles: challenges
and advances -- a systematic review | cs.RO cs.SY eess.SY | The increasing use and implementation of Autonomous Surface Vessels (ASVs)
for various activities in maritime environments is expected to drive a rise in
developments and research on their control. Particularly, the coordination of
multiple ASVs presents novel challenges and opportunities, requiring
interdisciplinary... |
2502.10089 | A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS
ACAM for Energy-Efficient Inference | cs.LG cs.AI cs.AR | In recent years, the development of smart edge computing systems to process
information locally is on the rise. Many near-sensor machine learning (ML)
approaches have been implemented to introduce accurate and energy efficient
template matching operations in resource-constrained edge sensing systems, such
as wearable... |
2502.10090 | Manual2Skill: Learning to Read Manuals and Acquire Robotic Skills for
Furniture Assembly Using Vision-Language Models | cs.RO cs.AI | Humans possess an extraordinary ability to understand and execute complex
manipulation tasks by interpreting abstract instruction manuals. For robots,
however, this capability remains a substantial challenge, as they cannot
interpret abstract instructions and translate them into executable actions. In
this paper, we ... |
2502.10091 | ELAA-ISAC: Environmental Mapping Utilizing the LoS State of
Communication Channel | cs.IT eess.SP math.IT | In this paper, a novel environmental mapping method is proposed to outline
the indoor layout utilizing the line-of-sight (LoS) state information of
extremely large aperture array (ELAA) channels. It leverages the spatial
resolution provided by ELAA and the mobile terminal (MT)'s mobility to infer
the presence and loc... |
2502.10092 | A novel approach to data generation in generative model | cs.LG cs.AI | Variational Autoencoders (VAEs) and other generative models are widely
employed in artificial intelligence to synthesize new data. However, current
approaches rely on Euclidean geometric assumptions and statistical
approximations that fail to capture the structured and emergent nature of data
generation. This paper i... |
2502.10095 | Representation Learning on Out of Distribution in Tabular Data | cs.LG | The open-world assumption in model development suggests that a model might
lack sufficient information to adequately handle data that is entirely distinct
or out of distribution (OOD). While deep learning methods have shown promising
results in handling OOD data through generalization techniques, they often
require s... |
2502.10097 | Causal Information Prioritization for Efficient Reinforcement Learning | cs.AI cs.LG | Current Reinforcement Learning (RL) methods often suffer from
sample-inefficiency, resulting from blind exploration strategies that neglect
causal relationships among states, actions, and rewards. Although recent causal
approaches aim to address this problem, they lack grounded modeling of
reward-guided causal unders... |
2502.10100 | Statistical data analysis for Tourism in Poland in R Programming
Environment | math.NA cs.CE cs.ET cs.NA cs.PL | This study utilises the R programming language for statistical data analysis
to understand Tourism dynamics in Poland. It focuses on methods for data
visualisation, multivariate statistics, and hypothesis testing. To investigate
the expenditure behavior of tourist, spending patterns, correlations, and
associations am... |
2502.10106 | Data-Adaptive Low-Rank Sparse Subspace Clustering | cs.LG | Low-rank sparse subspace clustering (LRSSC) algorithms built on
self-expressive model effectively capture both the global and local structure
of the data. However, existing solutions, primarily based on proximal operators
associated with Sp/Lp , p e {0, 1/2, 2/3, 1}, norms are not data-adaptive. In
this work, we prop... |
2502.10108 | NeuroXVocal: Detection and Explanation of Alzheimer's Disease through
Non-invasive Analysis of Picture-prompted Speech | cs.LG q-bio.NC | The early diagnosis of Alzheimer's Disease (AD) through non invasive methods
remains a significant healthcare challenge. We present NeuroXVocal, a novel
dual-component system that not only classifies but also explains potential AD
cases through speech analysis. The classification component (Neuro) processes
three dis... |
2502.10111 | COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks
via Node Feature and Structural Perturbations | cs.LG | Counterfactual explanations have emerged as a powerful tool to unveil the
opaque decision-making processes of graph neural networks (GNNs). However,
existing techniques primarily focus on edge modifications, often overlooking
the crucial role of node feature perturbations in shaping model predictions. To
address this... |
2502.10112 | Accelerometry-based Energy Expenditure Estimation During Activities of
Daily Living: A Comparison Among Different Accelerometer Compositions | cs.LG | Physical activity energy expenditure (PAEE) can be measured from
breath-by-breath respiratory data, which can serve as a reference.
Alternatively, PAEE can be predicted from the body movements, which can be
measured and estimated with accelerometers. The body center of mass (COM)
acceleration reflects the movements o... |
2502.10113 | Strain-Induced Optical and Molecular Transformations in PET Films for
Organic Electronic Applications | physics.app-ph cs.SY eess.SY physics.optics | Poly(ethylene terephthalate) (PET) films are widely used in flexible
electronics and optoelectronics, where their mechanical durability and optical
performance under strain are essential for device reliability. This study
investigates the impact of applied mechanical strain on the optical and
molecular properties of ... |
2502.10118 | Image Embedding Sampling Method for Diverse Captioning | cs.CV cs.AI | Image Captioning for state-of-the-art VLMs has significantly improved over
time; however, this comes at the cost of increased computational complexity,
making them less accessible for resource-constrained applications such as
mobile devices and assistive technologies. Alternatively, smaller VLMs
prioritize high-level... |
2502.10119 | SeWA: Selective Weight Average via Probabilistic Masking | cs.LG | Weight averaging has become a standard technique for enhancing model
performance. However, methods such as Stochastic Weight Averaging (SWA) and
Latest Weight Averaging (LAWA) often require manually designed procedures to
sample from the training trajectory, and the results depend heavily on
hyperparameter tuning. To... |
2502.10120 | Compress image to patches for Vision Transformer | cs.CV | The Vision Transformer (ViT) has made significant strides in the field of
computer vision. However, as the depth of the model and the resolution of the
input images increase, the computational cost associated with training and
running ViT models has surged dramatically. This paper proposes a hybrid model
based on CNN... |
2502.10122 | Modern Hopfield Networks with Continuous-Time Memories | cs.LG | Recent research has established a connection between modern Hopfield networks
(HNs) and transformer attention heads, with guarantees of exponential storage
capacity. However, these models still face challenges scaling storage
efficiently. Inspired by psychological theories of continuous neural resource
allocation in ... |
2502.10125 | Learning Relational Tabular Data without Shared Features | cs.LG cs.AI | Learning relational tabular data has gained significant attention recently,
but most studies focus on single tables, overlooking the potential of
cross-table learning. Cross-table learning, especially in scenarios where
tables lack shared features and pre-aligned data, offers vast opportunities but
also introduces su... |
2502.10127 | Leveraging V2X for Collaborative HD Maps Construction Using Scene Graph
Generation | cs.CV | High-Definition (HD) maps play a crucial role in autonomous vehicle
navigation, complementing onboard perception sensors for improved accuracy and
safety. Traditional HD map generation relies on dedicated mapping vehicles,
which are costly and fail to capture real-time infrastructure changes. This
paper presents HDMa... |
2502.10138 | Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with
Linear Function Approximation | cs.LG | We study the reinforcement learning (RL) problem in a constrained Markov
decision process (CMDP), where an agent explores the environment to maximize
the expected cumulative reward while satisfying a single constraint on the
expected total utility value in every episode. While this problem is well
understood in the t... |
2502.10140 | Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of
Small Multilingual Language Models for Low-Resource Languages | cs.CL | Low-resource languages (LRLs) face significant challenges in natural language
processing (NLP) due to limited data. While current state-of-the-art large
language models (LLMs) still struggle with LRLs, smaller multilingual models
(mLMs) such as mBERT and XLM-R offer greater promise due to a better fit of
their capaci... |
2502.10141 | Pangraphs as models of higher-order interactions | physics.soc-ph cs.SI math.CO q-bio.MN q-bio.PE | Graphs depict pairwise relationships between objects within a system.
Higher-order interactions (HOIs), which involve more than two objects
simultaneously, are common in nature. Such interactions can change the
stability of a complex system. Hypergraphs can represent an HOI as an arbitrary
subset of vertices. However... |
2502.10145 | Interpretable Concept-based Deep Learning Framework for Multimodal Human
Behavior Modeling | cs.CV cs.MM | In the contemporary era of intelligent connectivity, Affective Computing
(AC), which enables systems to recognize, interpret, and respond to human
behavior states, has become an integrated part of many AI systems. As one of
the most critical components of responsible AI and trustworthiness in all
human-centered syste... |
2502.10148 | Cooperative Multi-Agent Planning with Adaptive Skill Synthesis | cs.AI cs.MA | Despite much progress in training distributed artificial intelligence (AI),
building cooperative multi-agent systems with multi-agent reinforcement
learning (MARL) faces challenges in sample efficiency, interpretability, and
transferability. Unlike traditional learning-based methods that require
extensive interaction... |
2502.10151 | Semantica: Decentralized Search using a LLM-Guided Semantic Tree Overlay | cs.IR cs.DC cs.NI cs.SY eess.SY | Centralized search engines are key for the Internet, but lead to undesirable
concentration of power. Decentralized alternatives fail to offer equal document
retrieval accuracy and speed. Nevertheless, Semantic Overlay Networks can come
close to the performance of centralized solutions when the semantics of
documents ... |
2502.10154 | Video Soundtrack Generation by Aligning Emotions and Temporal Boundaries | cs.SD cs.AI cs.LG cs.MM eess.AS eess.IV | We introduce EMSYNC, a video-based symbolic music generation model that
aligns music with a video's emotional content and temporal boundaries. It
follows a two-stage framework, where a pretrained video emotion classifier
extracts emotional features, and a conditional music generator produces MIDI
sequences guided by ... |
2502.10156 | MonoForce: Learnable Image-conditioned Physics Engine | cs.RO cs.CV | We propose a novel model for the prediction of robot trajectories on rough
offroad terrain from the onboard camera images. This model enforces the laws of
classical mechanics through a physics-aware neural symbolic layer while
preserving the ability to learn from large-scale data as it is end-to-end
differentiable. T... |
2502.10157 | SessionRec: Next Session Prediction Paradigm For Generative Sequential
Recommendation | cs.IR cs.AI | We introduce SessionRec, a novel next-session prediction paradigm (NSPP) for
generative sequential recommendation, addressing the fundamental misalignment
between conventional next-item prediction paradigm (NIPP) and real-world
recommendation scenarios. Unlike NIPP's item-level autoregressive generation
that contradi... |
2502.10158 | Combinatorial Reinforcement Learning with Preference Feedback | stat.ML cs.LG | In this paper, we consider combinatorial reinforcement learning with
preference feedback, where a learning agent sequentially offers an action--an
assortment of multiple items to--a user, whose preference feedback follows a
multinomial logistic (MNL) model. This framework allows us to model real-world
scenarios, part... |
2502.10162 | Revisiting Generalization Power of a DNN in Terms of Symbolic
Interactions | cs.LG cs.AI cs.CL cs.CV | This paper aims to analyze the generalization power of deep neural networks
(DNNs) from the perspective of interactions. Unlike previous analysis of a
DNN's generalization power in a highdimensional feature space, we find that the
generalization power of a DNN can be explained as the generalization power of
the inter... |
2502.10163 | Enhancing anomaly detection with topology-aware autoencoders | hep-ph cs.LG hep-ex | Anomaly detection in high-energy physics is essential for identifying new
physics beyond the Standard Model. Autoencoders provide a signal-agnostic
approach but are limited by the topology of their latent space. This work
explores topology-aware autoencoders, embedding phase-space distributions onto
compact manifolds... |
2502.10173 | Agentic End-to-End De Novo Protein Design for Tailored Dynamics Using a
Language Diffusion Model | q-bio.BM cond-mat.mes-hall cond-mat.mtrl-sci cs.LG | Proteins are dynamic molecular machines whose biological functions, spanning
enzymatic catalysis, signal transduction, and structural adaptation, are
intrinsically linked to their motions. Designing proteins with targeted dynamic
properties, however, remains a challenge due to the complex, degenerate
relationships be... |
2502.10174 | Technical Risks of (Lethal) Autonomous Weapons Systems | cs.CY cs.AI cs.SY eess.SY | The autonomy and adaptability of (Lethal) Autonomous Weapons Systems, (L)AWS
in short, promise unprecedented operational capabilities, but they also
introduce profound risks that challenge the principles of control,
accountability, and stability in international security. This report outlines
the key technological ri... |
2502.10177 | STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task
Planning | cs.AI | A key objective of embodied intelligence is enabling agents to perform
long-horizon tasks in dynamic environments while maintaining robust
decision-making and adaptability. To achieve this goal, we propose the
Spatio-Temporal Memory Agent (STMA), a novel framework designed to enhance task
planning and execution by in... |
2502.10178 | From Markov to Laplace: How Mamba In-Context Learns Markov Chains | cs.LG cs.AI cs.IT math.IT | While transformer-based language models have driven the AI revolution thus
far, their computational complexity has spurred growing interest in viable
alternatives, such as structured state space sequence models (SSMs) and
Selective SSMs. Among these, Mamba (S6) and its variant Mamba-2 have shown
remarkable inference ... |
2502.10180 | Safe platooning control of connected and autonomous vehicles on curved
multi-lane roads | eess.SY cs.SY | This paper investigates the safe platoon formation tracking and merging
control problem of connected and automated vehicles (CAVs) on curved multi-lane
roads. The first novelty is the separation of the control designs into two
distinct parts: a lateral control law that ensures a geometrical convergence
towards the re... |
2502.10183 | Doing More With Less: Towards More Data-Efficient Syndrome-Based Neural
Decoders | cs.IT math.IT | While significant research efforts have been directed toward developing more
capable neural decoding architectures, comparatively little attention has been
paid to the quality of training data. In this study, we address the challenge
of constructing effective training datasets to maximize the potential of
existing sy... |
2502.10184 | Realistic Evaluation of Deep Partial-Label Learning Algorithms | cs.LG | Partial-label learning (PLL) is a weakly supervised learning problem in which
each example is associated with multiple candidate labels and only one is the
true label. In recent years, many deep PLL algorithms have been developed to
improve model performance. However, we find that some early developed
algorithms are ... |
2502.10185 | A Powerful Random Forest Featuring Linear Extensions (RaFFLE) | cs.LG | Random forests are widely used in regression. However, the decision trees
used as base learners are poor approximators of linear relationships. To
address this limitation we propose RaFFLE (Random Forest Featuring Linear
Extensions), a novel framework that integrates the recently developed PILOT
trees (Piecewise Line... |
2502.10187 | Reinforcement Learning based Constrained Optimal Control: an
Interpretable Reward Design | eess.SY cs.SY | This paper presents an interpretable reward design framework for
reinforcement learning based constrained optimal control problems with state
and terminal constraints. The problem is formalized within a standard partially
observable Markov decision process framework. The reward function is
constructed from four weigh... |
2502.10192 | A Note on "Constructing Bent Functions Outside the Maiorana-McFarland
Class Using a General Form of Rothaus" | cs.IT math.IT | In 2017, Zhang et al. proposed a question (not open problem) and two open
problems in [IEEE TIT 63 (8): 5336--5349, 2017] about constructing bent
functions by using Rothaus' construction. In this note, we prove that the
sufficient conditions of Rothaus' construction are also necessary, which
answers their question. B... |
2502.10193 | Merging public elementary schools to reduce racial/ethnic segregation | cs.CY cs.AI | Diverse schools can help address implicit biases and increase empathy, mutual
respect, and reflective thought by fostering connections between students from
different racial/ethnic, socioeconomic, and other backgrounds. Unfortunately,
demographic segregation remains rampant in US public schools, despite over 70
years... |
2502.10195 | Exploring the Camera Bias of Person Re-identification | cs.CV cs.AI cs.LG | We empirically investigate the camera bias of person re-identification (ReID)
models. Previously, camera-aware methods have been proposed to address this
issue, but they are largely confined to training domains of the models. We
measure the camera bias of ReID models on unseen domains and reveal that camera
bias beco... |
2502.10197 | MathConstruct: Challenging LLM Reasoning with Constructive Proofs | cs.AI | While Large Language Models (LLMs) demonstrate impressive performance in
mathematics, existing math benchmarks come with significant limitations. Many
focus on problems with fixed ground-truth answers, and are often saturated due
to problem simplicity or the viability of guessing or memorization. Crucially,
they capt... |
2502.10200 | Dynamic Reinforcement Learning for Actors | cs.LG cs.AI cs.NE | Dynamic Reinforcement Learning (Dynamic RL), proposed in this paper, directly
controls system dynamics, instead of the actor (action-generating neural
network) outputs at each moment, bringing about a major qualitative shift in
reinforcement learning (RL) from static to dynamic. The actor is initially
designed to gen... |
2502.10201 | Prediction hubs are context-informed frequent tokens in LLMs | cs.CL cs.AI | Hubness, the tendency for few points to be among the nearest neighbours of a
disproportionate number of other points, commonly arises when applying standard
distance measures to high-dimensional data, often negatively impacting
distance-based analysis. As autoregressive large language models (LLMs) operate
on high-di... |
2502.10202 | Can Post-Training Quantization Benefit from an Additional QLoRA
Integration? | cs.CL | Large language models (LLMs) have transformed natural language processing but
pose significant challenges for real-world deployment. These models necessitate
considerable computing resources, which can be costly and frequently
unavailable. Model compression techniques such as quantization are often
leveraged to allev... |
2502.10203 | AI-in-the-Loop Sensing and Communication Joint Design for Edge
Intelligence | cs.LG cs.DC | Recent breakthroughs in artificial intelligence (AI), wireless
communications, and sensing technologies have accelerated the evolution of edge
intelligence. However, conventional systems still grapple with issues such as
low communication efficiency, redundant data acquisition, and poor model
generalization. To overc... |
2502.10205 | Looking around you: external information enhances representations for
event sequences | cs.LG | Representation learning produces models in different domains, such as store
purchases, client transactions, and general people's behaviour. However, such
models for sequential data usually process a single sequence, ignoring context
from other relevant ones, even in domains with rapidly changing external
environments... |
2502.10207 | RIPOST: Two-Phase Private Decomposition for Multidimensional Data | cs.DB | Differential privacy (DP) is considered as the gold standard for data
privacy. While the problem of answering simple queries and functions under DP
guarantees has been thoroughly addressed in recent years, the problem of
releasing multidimensional data under DP remains challenging. In this paper, we
focus on this pro... |
2502.10208 | SGS-GNN: A Supervised Graph Sparsification method for Graph Neural
Networks | cs.LG | We propose SGS-GNN, a novel supervised graph sparsifier that learns the
sampling probability distribution of edges and samples sparse subgraphs of a
user-specified size to reduce the computational costs required by GNNs for
inference tasks on large graphs. SGS-GNN employs regularizers in the loss
function to enhance ... |
2502.10209 | Mutual Coupling in Holographic MIMO: Physical Modeling and
Information-Theoretic Analysis | cs.IT math.IT | This paper presents a comprehensive framework for holographic multiantenna
communication, a paradigm that integrates both wide apertures and closely
spaced antennas relative to the wavelength. The presented framework is
physically grounded, enabling information-theoretic analyses that inherently
incorporate correlati... |
2502.10211 | Control-flow anomaly detection by process mining-based feature
extraction and dimensionality reduction | cs.LG | The business processes of organizations may deviate from normal control flow
due to disruptive anomalies, including unknown, skipped, and wrongly-ordered
activities. To identify these control-flow anomalies, process mining can check
control-flow correctness against a reference process model through conformance
checki... |
2502.10214 | Mapping bathymetry of inland water bodies on the North Slope of Alaska
with Landsat using Random Forest | cs.CV cs.LG | The North Slope of Alaska is dominated by small waterbodies that provide
critical ecosystem services for local population and wildlife. Detailed
information on the depth of the waterbodies is scarce due to the challenges
with collecting such information. In this work we have trained a machine
learning (Random Forest ... |
2502.10215 | Do Large Language Models Reason Causally Like Us? Even Better? | cs.AI cs.LG | Causal reasoning is a core component of intelligence. Large language models
(LLMs) have shown impressive capabilities in generating human-like text,
raising questions about whether their responses reflect true understanding or
statistical patterns. We compared causal reasoning in humans and four LLMs
using tasks base... |
2502.10216 | Forget the Data and Fine-Tuning! Just Fold the Network to Compress | cs.LG cs.AI | We introduce model folding, a novel data-free model compression technique
that merges structurally similar neurons across layers, significantly reducing
the model size without the need for fine-tuning or access to training data.
Unlike existing methods, model folding preserves data statistics during
compression by le... |
2502.10218 | Integrated Multi-Simulation Environments for Aerial Robotics Research | cs.RO | Simulation frameworks play a pivotal role in the safe development of robotic
applications. However, often different components of an envisioned robotic
system are best simulated in different environments/simulators. This poses a
significant challenge in simulating the entire project into a single integrated
robotic f... |
2502.10220 | Optimal and Coordinated Voltage Control: Case Study on a 132 kV
Norwegian Grid Subsystem | eess.SY cs.SY | This work presents a framework for dynamic performance assessment of the
higher layers in the hierarchical voltage regulation scheme, with case studies
applied to specific areas of the Norwegian grid. Unlike the primary (PVR)
level, the secondary (SVR) and tertiary (TVR) levels are not tuned to a single
device at a t... |
2502.10224 | Comparison of Deep Recurrent Neural Networks and Bayesian Neural
Networks for Detecting Electric Motor Damage Through Sound Signal Analysis | cs.LG | Fault detection in electric motors is a critical challenge in various
industries, where failures can result in significant operational disruptions.
This study investigates the use of Recurrent Neural Networks (RNNs) and
Bayesian Neural Networks (BNNs) for diagnosing motor damage using acoustic
signal analysis. A nove... |
2502.10226 | A Multiagent Path Search Algorithm for Large-Scale Coalition Structure
Generation | cs.MA cs.AI cs.GT | Coalition structure generation (CSG), i.e. the problem of optimally
partitioning a set of agents into coalitions to maximize social welfare, is a
fundamental computational problem in multiagent systems. This problem is
important for many applications where small run times are necessary, including
transportation and d... |
2502.10230 | ProReco: A Process Discovery Recommender System | cs.LG cs.IR | Process discovery aims to automatically derive process models from historical
execution data (event logs). While various process discovery algorithms have
been proposed in the last 25 years, there is no consensus on a dominating
discovery algorithm. Selecting the most suitable discovery algorithm remains a
challenge ... |
2502.10233 | Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via
Hierarchical and Parallel Decoding | cs.MA cs.LG stat.ML | The Mixed-Shelves Picker Routing Problem (MSPRP) is a fundamental challenge
in warehouse logistics, where pickers must navigate a mixed-shelves environment
to retrieve SKUs efficiently. Traditional heuristics and optimization-based
approaches struggle with scalability, while recent machine learning methods
often rely... |
2502.10235 | AdaPTS: Adapting Univariate Foundation Models to Probabilistic
Multivariate Time Series Forecasting | stat.ML cs.LG | Pre-trained foundation models (FMs) have shown exceptional performance in
univariate time series forecasting tasks. However, several practical challenges
persist, including managing intricate dependencies among features and
quantifying uncertainty in predictions. This study aims to tackle these
critical limitations b... |
2502.10236 | Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise
Control | cs.LG cs.AI | Diffusion Probabilistic Models (DPMs) are powerful generative models that
have achieved unparalleled success in a number of generative tasks. In this
work, we aim to build inductive biases into the training and sampling of
diffusion models to better accommodate the target distribution of the data to
model. For topolo... |
2502.10239 | Efficient Zero-Order Federated Finetuning of Language Models for
Resource-Constrained Devices | cs.LG cs.AI | Federated fine-tuning offers a promising approach for tuning Large Language
Models (LLMs) on edge devices while preserving data privacy. However,
fine-tuning these models on edge devices remains challenging due to high
memory, communication, and computational demands. Zero-order optimization with
task alignment provi... |
2502.10243 | Safety Blind Spot in Remote Driving: Considerations for Risk Assessment
of Connection Loss Fallback Strategies | eess.SY cs.SY | As part of the overall goal of driverless road vehicles, remote driving is a
major emerging field of research of its own. Current remote driving concepts
for public road traffic often establish a fallback strategy of immediate
braking to a standstill in the event of a connection loss. This may seem like
the most logi... |
2502.10248 | Step-Video-T2V Technical Report: The Practice, Challenges, and Future of
Video Foundation Model | cs.CV cs.CL | We present Step-Video-T2V, a state-of-the-art text-to-video pre-trained model
with 30B parameters and the ability to generate videos up to 204 frames in
length. A deep compression Variational Autoencoder, Video-VAE, is designed for
video generation tasks, achieving 16x16 spatial and 8x temporal compression
ratios, wh... |
2502.10250 | VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision
Language Models | cs.CL cs.CV | Vision-language models (VLMs) excel in various visual benchmarks but are
often constrained by the lack of high-quality visual fine-tuning data. To
address this challenge, we introduce VisCon-100K, a novel dataset derived from
interleaved image-text web documents. Our approach transforms 45K web documents
from the OBE... |
2502.10258 | PromptArtisan: Multi-instruction Image Editing in Single Pass with
Complete Attention Control | cs.CV cs.HC | We present PromptArtisan, a groundbreaking approach to multi-instruction
image editing that achieves remarkable results in a single pass, eliminating
the need for time-consuming iterative refinement. Our method empowers users to
provide multiple editing instructions, each associated with a specific mask
within the im... |
2502.10259 | MITO: Enabling Non-Line-of-Sight Perception using Millimeter-waves
through Real-World Datasets and Simulation Tools | cs.CV | We present MITO, the first dataset of multi-spectral millimeter-wave (mmWave)
images of everyday objects. Unlike visible light, mmWave signals can image
through everyday occlusions (e.g., cardboard boxes, fabric, plastic). However,
due to the dearth of publicly-available mmWave images and the interdisciplinary
challe... |
2502.10263 | Large Language Models and Synthetic Data for Monitoring Dataset Mentions
in Research Papers | cs.CL cs.AI cs.CY cs.DB cs.LG | Tracking how data is mentioned and used in research papers provides critical
insights for improving data discoverability, quality, and production. However,
manually identifying and classifying dataset mentions across vast academic
literature is resource-intensive and not scalable. This paper presents a
machine learni... |
2502.10266 | Are Large Language Models the future crowd workers of Linguistics? | cs.CL cs.AI | Data elicitation from human participants is one of the core data collection
strategies used in empirical linguistic research. The amount of participants in
such studies may vary considerably, ranging from a handful to crowdsourcing
dimensions. Even if they provide resourceful extensive data, both of these
settings co... |
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