id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.01107 | GTG: Generalizable Trajectory Generation Model for Urban Mobility | cs.LG | Trajectory data mining is crucial for smart city management. However,
collecting large-scale trajectory datasets is challenging due to factors such
as commercial conflicts and privacy regulations. Therefore, we urgently need
trajectory generation techniques to address this issue. Existing trajectory
generation method... |
2502.01108 | Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for
Wearable Applications Across Lab and Field Settings | cs.LG cs.AI eess.SP | Photoplethysmography (PPG)-based foundation models are gaining traction due
to the widespread use of PPG in biosignal monitoring and their potential to
generalize across diverse health applications. In this paper, we introduce
Pulse-PPG, the first open-source PPG foundation model trained exclusively on
raw PPG data c... |
2502.01111 | A generative foundation model for an all-in-one seismic processing
framework | physics.geo-ph cs.AI | Seismic data often face challenges in their utilization due to noise
contamination, incomplete acquisition, and limited low-frequency information,
which hinder accurate subsurface imaging and interpretation. Traditional
processing methods rely heavily on task-specific designs to address these
challenges and fail to a... |
2502.01113 | GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation | cs.IR cs.AI cs.CL | Retrieval-augmented generation (RAG) has proven effective in integrating
knowledge into large language models (LLMs). However, conventional RAGs
struggle to capture complex relationships between pieces of knowledge, limiting
their performance in intricate reasoning that requires integrating knowledge
from multiple so... |
2502.01116 | Picky LLMs and Unreliable RMs: An Empirical Study on Safety Alignment
after Instruction Tuning | cs.AI cs.CL cs.LG | Large language models (LLMs) have emerged as powerful tools for addressing a
wide range of general inquiries and tasks. Despite this, fine-tuning aligned
LLMs on smaller, domain-specific datasets, critical to adapting them to
specialized tasks, can inadvertently degrade their safety alignment, even when
the datasets ... |
2502.01117 | Learning to Learn Weight Generation via Trajectory Diffusion | cs.LG cs.AI cs.CV | Diffusion-based algorithms have emerged as promising techniques for weight
generation, particularly in scenarios like multi-task learning that require
frequent weight updates. However, existing solutions suffer from limited
cross-task transferability. In addition, they only utilize optimal weights as
training samples... |
2502.01118 | Large Language Model-Enhanced Multi-Armed Bandits | cs.LG cs.AI | Large language models (LLMs) have been adopted to solve sequential
decision-making tasks such as multi-armed bandits (MAB), in which an LLM is
directly instructed to select the arms to pull in every iteration. However,
this paradigm of direct arm selection using LLMs has been shown to be
suboptimal in many MAB tasks.... |
2502.01122 | Learning Efficient Positional Encodings with Graph Neural Networks | cs.LG | Positional encodings (PEs) are essential for effective graph representation
learning because they provide position awareness in inherently
position-agnostic transformer architectures and increase the expressive
capacity of Graph Neural Networks (GNNs). However, designing powerful and
efficient PEs for graphs poses si... |
2502.01126 | Language Models Prefer What They Know: Relative Confidence Estimation
via Confidence Preferences | cs.CL | Language models (LMs) should provide reliable confidence estimates to help
users detect mistakes in their outputs and defer to human experts when
necessary. Asking a language model to assess its confidence ("Score your
confidence from 0-1.") is a natural way of evaluating its uncertainty. However,
models struggle to ... |
2502.01127 | The Battling Influencers Game: Nash Equilibria Structure of a Potential
Game and Implications to Value Alignment | cs.GT cs.AI | When multiple influencers attempt to compete for a receiver's attention,
their influencing strategies must account for the presence of one another. We
introduce the Battling Influencers Game (BIG), a multi-player simultaneous-move
general-sum game, to provide a game-theoretic characterization of this social
phenomeno... |
2502.01128 | C codegen considered unnecessary: go directly to binary, do not pass C.
Compilation of Julia code for deployment in model-based engineering | eess.SY cs.SY | Since time immemorial an old adage has always seemed to ring true: you cannot
use a high-level productive programming language like Python or R for real-time
control and embedded-systems programming, you must rewrite your program in C.
We present a counterexample to this mantra by demonstrating how recent compiler
de... |
2502.01129 | Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless
Networks | cs.DC cs.AI cs.ET cs.LG | This report investigates the application of deep reinforcement learning (DRL)
algorithms for dynamic resource allocation in wireless communication systems.
An environment that includes a base station, multiple antennas, and user
equipment is created. Using the RLlib library, various DRL algorithms such as
Deep Q-Netw... |
2502.01131 | Simple Linear Neuron Boosting | cs.LG stat.ML | Given a differentiable network architecture and loss function, we revisit
optimizing the network's neurons in function space using Boosted
Backpropagation (Grubb & Bagnell, 2010), in contrast to optimizing in parameter
space. From this perspective, we reduce descent in the space of linear
functions that optimizes the... |
2502.01137 | Self-Organizing Interaction Spaces: A Framework for Engineering
Pervasive Applications in Mobile and Distributed Environments | cs.DC cs.AI cs.LG cs.SE | The rapid adoption of pervasive and mobile computing has led to an
unprecedented rate of data production and consumption by mobile applications at
the network edge. These applications often require interactions such as data
exchange, behavior coordination, and collaboration, which are typically
mediated by cloud serv... |
2502.01141 | Beyond Yes or No: Predictive Compliance Monitoring Approaches for
Quantifying the Magnitude of Compliance Violations | cs.LG cs.AI | Most existing process compliance monitoring approaches detect compliance
violations in an ex post manner. Only predicate prediction focuses on
predicting them. However, predicate prediction provides a binary yes/no notion
of compliance, lacking the ability to measure to which extent an ongoing
process instance deviat... |
2502.01142 | DeepRAG: Thinking to Retrieval Step by Step for Large Language Models | cs.AI cs.CL cs.IR | Large Language Models (LLMs) have shown remarkable potential in reasoning
while they still suffer from severe factual hallucinations due to timeliness,
accuracy, and coverage of parametric knowledge. Meanwhile, integrating
reasoning with retrieval-augmented generation (RAG) remains challenging due to
ineffective task... |
2502.01143 | ASAP: Aligning Simulation and Real-World Physics for Learning Agile
Humanoid Whole-Body Skills | cs.RO cs.AI cs.LG cs.SY eess.SY | Humanoid robots hold the potential for unparalleled versatility in performing
human-like, whole-body skills. However, achieving agile and coordinated
whole-body motions remains a significant challenge due to the dynamics mismatch
between simulation and the real world. Existing approaches, such as system
identificatio... |
2502.01145 | Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task
Learning: A Sheaf-Theoretic Approach | cs.LG | Federated multi-task learning (FMTL) aims to simultaneously learn multiple
related tasks across clients without sharing sensitive raw data. However, in
the decentralized setting, existing FMTL frameworks are limited in their
ability to capture complex task relationships and handle feature and sample
heterogeneity acr... |
2502.01146 | Quantum Machine Learning: A Hands-on Tutorial for Machine Learning
Practitioners and Researchers | quant-ph cs.AI cs.LG | This tutorial intends to introduce readers with a background in AI to quantum
machine learning (QML) -- a rapidly evolving field that seeks to leverage the
power of quantum computers to reshape the landscape of machine learning. For
self-consistency, this tutorial covers foundational principles, representative
QML al... |
2502.01152 | Gradient Norm-based Fine-Tuning for Backdoor Defense in Automatic Speech
Recognition | cs.SD cs.LG eess.AS | Backdoor attacks have posed a significant threat to the security of deep
neural networks (DNNs). Despite considerable strides in developing defenses
against backdoor attacks in the visual domain, the specialized defenses for the
audio domain remain empty. Furthermore, the defenses adapted from the visual to
audio dom... |
2502.01154 | Jailbreaking with Universal Multi-Prompts | cs.CL cs.AI cs.CR cs.LG | Large language models (LLMs) have seen rapid development in recent years,
revolutionizing various applications and significantly enhancing convenience
and productivity. However, alongside their impressive capabilities, ethical
concerns and new types of attacks, such as jailbreaking, have emerged. While
most prompting... |
2502.01156 | On the impact of the parametrization of deep convolutional neural
networks on post-training quantization | cs.IT math.IT | This paper introduces novel theoretical approximation bounds for the output
of quantized neural networks, with a focus on convolutional neural networks
(CNN). By considering layerwise parametrization and focusing on the
quantization of weights, we provide bounds that gain several orders of
magnitude compared to state... |
2502.01157 | Radiant Foam: Real-Time Differentiable Ray Tracing | cs.CV | Research on differentiable scene representations is consistently moving
towards more efficient, real-time models. Recently, this has led to the
popularization of splatting methods, which eschew the traditional ray-based
rendering of radiance fields in favor of rasterization. This has yielded a
significant improvement... |
2502.01158 | MIND: Modality-Informed Knowledge Distillation Framework for Multimodal
Clinical Prediction Tasks | cs.LG cs.AI cs.CV | Multimodal fusion leverages information across modalities to learn better
feature representations with the goal of improving performance in fusion-based
tasks. However, multimodal datasets, especially in medical settings, are
typically smaller than their unimodal counterparts, which can impede the
performance of mult... |
2502.01159 | AtmosSci-Bench: Evaluating the Recent Advance of Large Language Model
for Atmospheric Science | cs.LG cs.AI | The rapid advancements in large language models (LLMs), particularly in their
reasoning capabilities, hold transformative potential for addressing complex
challenges in atmospheric science. However, leveraging LLMs effectively in this
domain requires a robust and comprehensive evaluation benchmark. To address
this ne... |
2502.01160 | Scalable Precise Computation of Shannon Entropy | cs.AI cs.IT math.IT | Quantitative information flow analyses (QIF) are a class of techniques for
measuring the amount of confidential information leaked by a program to its
public outputs.
Shannon entropy is an important method to quantify the amount of leakage in
QIF.
This paper focuses on the programs modeled in Boolean constraints ... |
2502.01167 | ConditionNET: Learning Preconditions and Effects for Execution
Monitoring | cs.RO cs.LG | The introduction of robots into everyday scenarios necessitates algorithms
capable of monitoring the execution of tasks. In this paper, we propose
ConditionNET, an approach for learning the preconditions and effects of actions
in a fully data-driven manner. We develop an efficient vision-language model
and introduce ... |
2502.01170 | Label Distribution Learning with Biased Annotations by Learning
Multi-Label Representation | cs.LG | Multi-label learning (MLL) has gained attention for its ability to represent
real-world data. Label Distribution Learning (LDL), an extension of MLL to
learning from label distributions, faces challenges in collecting accurate
label distributions. To address the issue of biased annotations, based on the
low-rank assu... |
2502.01171 | Efficient and Scalable Density Functional Theory Hamiltonian Prediction
through Adaptive Sparsity | cs.LG physics.comp-ph | Hamiltonian matrix prediction is pivotal in computational chemistry, serving
as the foundation for determining a wide range of molecular properties. While
SE(3) equivariant graph neural networks have achieved remarkable success in
this domain, their substantial computational cost-driven by high-order tensor
product (... |
2502.01172 | Towards Agile Swarming in Real World: Onboard Relative Localization with
Fast Tracking of Active Blinking Markers | cs.RO cs.CV | A novel onboard tracking approach enabling vision-based relative localization
and communication using Active blinking Marker Tracking (AMT) is introduced in
this article. Active blinking markers on multi-robot team members improve the
robustness of relative localization for aerial vehicles in tightly coupled
swarms d... |
2502.01177 | Insights from Network Science can advance Deep Graph Learning | cs.LG | Deep graph learning and network science both analyze graphs but approach
similar problems from different perspectives. Whereas network science focuses
on models and measures that reveal the organizational principles of complex
systems with explicit assumptions, deep graph learning focuses on flexible and
generalizabl... |
2502.01179 | Joint Localization and Activation Editing for Low-Resource Fine-Tuning | cs.CL cs.AI cs.LG | Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, are commonly
used to adapt LLMs. However, the effectiveness of standard PEFT methods is
limited in low-resource scenarios with only a few hundred examples. Recent
advances in interpretability research have inspired the emergence of activation
editing techn... |
2502.01180 | A Minimax Optimal Controller for Positive Systems | math.OC cs.SY eess.SY | We present an explicit solution to the discrete-time Bellman equation for
minimax optimal control of positive systems under unconstrained disturbances.
The primary contribution of our result relies on deducing a bound for the
disturbance penalty, which characterizes the existence of a finite solution to
the problem c... |
2502.01181 | BVINet: Unlocking Blind Video Inpainting with Zero Annotations | cs.CV | Video inpainting aims to fill in corrupted regions of the video with
plausible contents. Existing methods generally assume that the locations of
corrupted regions are known, focusing primarily on the "how to inpaint". This
reliance necessitates manual annotation of the corrupted regions using binary
masks to indicate... |
2502.01182 | A Single Model Ensemble Framework for Neural Machine Translation using
Pivot Translation | cs.CL cs.AI | Despite the significant advances in neural machine translation, performance
remains subpar for low-resource language pairs. Ensembling multiple systems is
a widely adopted technique to enhance performance, often accomplished by
combining probability distributions. However, the previous approaches face the
challenge o... |
2502.01183 | Enhancing Environmental Robustness in Few-shot Learning via Conditional
Representation Learning | cs.CV | Few-shot learning (FSL) has recently been extensively utilized to overcome
the scarcity of training data in domain-specific visual recognition. In
real-world scenarios, environmental factors such as complex backgrounds,
varying lighting conditions, long-distance shooting, and moving targets often
cause test images to... |
2502.01184 | FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence
Molecular Representation Learning | cs.LG cs.AI physics.chem-ph q-bio.QM | Molecular property prediction uses molecular structure to infer chemical
properties. Chemically interpretable representations that capture meaningful
intramolecular interactions enhance the usability and effectiveness of these
predictions. However, existing methods often rely on atom-based or rule-based
fragment toke... |
2502.01185 | Deep Active Speech Cancellation with Multi-Band Mamba Network | cs.SD cs.AI cs.LG eess.AS eess.SP | We present a novel deep learning network for Active Speech Cancellation
(ASC), advancing beyond Active Noise Cancellation (ANC) methods by effectively
canceling both noise and speech signals. The proposed Multi-Band Mamba
architecture segments input audio into distinct frequency bands, enabling
precise anti-signal ge... |
2502.01186 | A High-Accuracy SSIM-based Scoring System for Coin Die Link
Identification | cs.CV | The analyses of ancient coins, and especially the identification of those
struck with the same die, provides invaluable information for archaeologists
and historians. Nowadays, these die links are identified manually, which makes
the process laborious, if not impossible when big treasures are discovered as
the number... |
2502.01187 | Skewed Memorization in Large Language Models: Quantification and
Decomposition | cs.AI cs.CL cs.LG | Memorization in Large Language Models (LLMs) poses privacy and security
risks, as models may unintentionally reproduce sensitive or copyrighted data.
Existing analyses focus on average-case scenarios, often neglecting the highly
skewed distribution of memorization. This paper examines memorization in LLM
supervised f... |
2502.01188 | FairUDT: Fairness-aware Uplift Decision Trees | cs.LG stat.ML | Training data used for developing machine learning classifiers can exhibit
biases against specific protected attributes. Such biases typically originate
from historical discrimination or certain underlying patterns that
disproportionately under-represent minority groups, such as those identified by
their gender, reli... |
2502.01189 | Compressed Image Generation with Denoising Diffusion Codebook Models | eess.IV cs.AI cs.CV cs.IT eess.SP math.IT | We present a novel generative approach based on Denoising Diffusion Models
(DDMs), which produces high-quality image samples along with their losslessly
compressed bit-stream representations. This is obtained by replacing the
standard Gaussian noise sampling in the reverse diffusion with a selection of
noise samples ... |
2502.01190 | Dance recalibration for dance coherency with recurrent convolution block | cs.LG cs.AI | With the recent advancements in generative AI such as GAN, Diffusion, and
VAE, the use of generative AI for dance generation has seen significant
progress and received considerable interest. In this study, We propose R-Lodge,
an enhanced version of Lodge. R-Lodge incorporates Recurrent Sequential
Representation Learn... |
2502.01191 | Towards Robust and Reliable Concept Representations:
Reliability-Enhanced Concept Embedding Model | cs.CV | Concept Bottleneck Models (CBMs) aim to enhance interpretability by
predicting human-understandable concepts as intermediates for decision-making.
However, these models often face challenges in ensuring reliable concept
representations, which can propagate to downstream tasks and undermine
robustness, especially unde... |
2502.01194 | COVE: COntext and VEracity prediction for out-of-context images | cs.CL | Images taken out of their context are the most prevalent form of multimodal
misinformation. Debunking them requires (1) providing the true context of the
image and (2) checking the veracity of the image's caption. However, existing
automated fact-checking methods fail to tackle both objectives explicitly. In
this wor... |
2502.01197 | Multi-objective Evolution of Drone Morphology | cs.RO | The design of multicopter drones has remained almost the same since its
inception. While conventional designs, such as the quadcopter, work well in
many cases, they may not be optimal in specific environments or missions. This
paper revisits rotary drone design by exploring which body morphologies are
optimal for dif... |
2502.01199 | Nearly Lossless Adaptive Bit Switching | cs.CV cs.AI | Model quantization is widely applied for compressing and accelerating deep
neural networks (DNNs). However, conventional Quantization-Aware Training (QAT)
focuses on training DNNs with uniform bit-width. The bit-width settings vary
across different hardware and transmission demands, which induces considerable
trainin... |
2502.01201 | One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection | cs.CV | Traditional Anomaly Detection (AD) methods have predominantly relied on
unsupervised learning from extensive normal data. Recent AD methods have
evolved with the advent of large pre-trained vision-language models, enhancing
few-shot anomaly detection capabilities. However, these latest AD methods still
exhibit limita... |
2502.01203 | Theoretical Analysis of KL-regularized RLHF with Multiple Reference
Models | cs.LG stat.ML | Recent methods for aligning large language models (LLMs) with human feedback
predominantly rely on a single reference model, which limits diversity, model
overfitting, and underutilizes the wide range of available pre-trained models.
Incorporating multiple reference models has the potential to address these
limitatio... |
2502.01204 | Land Surface Temperature Super-Resolution with a Scale-Invariance-Free
Neural Approach: Application to MODIS | cs.LG cs.CV | Due to the trade-off between the temporal and spatial resolution of thermal
spaceborne sensors, super-resolution methods have been developed to provide
fine-scale Land SurfaceTemperature (LST) maps. Most of them are trained at low
resolution but applied at fine resolution, and so they require a
scale-invariance hypot... |
2502.01205 | OCR Error Post-Correction with LLMs in Historical Documents: No Free
Lunches | cs.CL | Optical Character Recognition (OCR) systems often introduce errors when
transcribing historical documents, leaving room for post-correction to improve
text quality. This study evaluates the use of open-weight LLMs for OCR error
correction in historical English and Finnish datasets. We explore various
strategies, incl... |
2502.01207 | Solgenia -- A Test Vessel Toward Energy-Efficient Autonomous Water Taxi
Applications | cs.RO cs.SY eess.SY | Autonomous surface vessels are a promising building block of the future's
transport sector and are investigated by research groups worldwide. This paper
presents a comprehensive and systematic overview of the autonomous research
vessel Solgenia including the latest investigations and recently presented
methods that c... |
2502.01208 | Almost Surely Safe Alignment of Large Language Models at Inference-Time | cs.LG cs.CL | Even highly capable large language models (LLMs) can produce biased or unsafe
responses, and alignment techniques, such as RLHF, aimed at mitigating this
issue, are expensive and prone to overfitting as they retrain the LLM. This
paper introduces a novel inference-time alignment approach that ensures LLMs
generate sa... |
2502.01210 | Modelling change in neural dynamics during phonetic accommodation | cs.CL | Short-term phonetic accommodation is a fundamental driver behind accent
change, but how does real-time input from another speaker's voice shape the
speech planning representations of an interlocutor? We advance a computational
model of change in phonetic representations during phonetic accommodation,
grounded in dyna... |
2502.01211 | Privilege Scores | cs.LG stat.ML | Bias-transforming methods of fairness-aware machine learning aim to correct a
non-neutral status quo with respect to a protected attribute (PA). Current
methods, however, lack an explicit formulation of what drives non-neutrality.
We introduce privilege scores (PS) to measure PA-related privilege by comparing
the mod... |
2502.01216 | Exploring Few-Shot Defect Segmentation in General Industrial Scenarios
with Metric Learning and Vision Foundation Models | cs.CV | Industrial defect segmentation is critical for manufacturing quality control.
Due to the scarcity of training defect samples, few-shot semantic segmentation
(FSS) holds significant value in this field. However, existing studies mostly
apply FSS to tackle defects on simple textures, without considering more
diverse sc... |
2502.01218 | Provable Ordering and Continuity in Vision-Language Pretraining for
Generalizable Embodied Agents | cs.RO cs.AI cs.CV cs.LG | Pre-training vision-language representations on human action videos has
emerged as a promising approach to reduce reliance on large-scale expert
demonstrations for training embodied agents. However, prior methods often
employ time contrastive learning based on goal-reaching heuristics,
progressively aligning language... |
2502.01220 | Language Models Struggle to Achieve a Consistent Temporal Representation
of Facts | cs.CL cs.LG | Language Models (LMs) have shown substantial improvements in handling factual
knowledge, yet their capability to consistently represent temporal facts, which
are valid only within specific timeframes, remains underexplored. To
investigate this, we introduce TimeStress, a novel dataset comprising 521K
statements on 20... |
2502.01225 | The dark deep side of DeepSeek: Fine-tuning attacks against the safety
alignment of CoT-enabled models | cs.CR cs.AI | Large language models are typically trained on vast amounts of data during
the pre-training phase, which may include some potentially harmful information.
Fine-tuning attacks can exploit this by prompting the model to reveal such
behaviours, leading to the generation of harmful content. In this paper, we
focus on inv... |
2502.01226 | Efficient Prior Selection in Gaussian Process Bandits with Thompson
Sampling | cs.LG stat.ML | Gaussian process (GP) bandits provide a powerful framework for solving
blackbox optimization of unknown functions. The characteristics of the unknown
function depends heavily on the assumed GP prior. Most work in the literature
assume that this prior is known but in practice this seldom holds. Instead,
practitioners ... |
2502.01228 | Soft Robot Localization Using Distributed Miniaturized Time-of-Flight
Sensors | cs.RO | Thanks to their compliance and adaptability, soft robots can be deployed to
perform tasks in constrained or complex environments. In these scenarios,
spatial awareness of the surroundings and the ability to localize the robot
within the environment represent key aspects. While state-of-the-art
localization techniques... |
2502.01229 | How Good are Learned Cost Models, Really? Insights from Query
Optimization Tasks | cs.DB | Traditionally, query optimizers rely on cost models to choose the best
execution plan from several candidates, making precise cost estimates critical
for efficient query execution. In recent years, cost models based on machine
learning have been proposed to overcome the weaknesses of traditional cost
models. While th... |
2502.01231 | Societal Attitudes Toward Service Robots: Adore, Abhor, Ignore, or
Unsure? | cs.RO | Societal or population-level attitudes are aggregated patterns of different
individual attitudes, representing collective general predispositions. As
service robots become ubiquitous, understanding attitudes towards them at the
population (vs. individual) level enables firms to expand robot services to a
broad (vs. n... |
2502.01232 | Efficient rule induction by ignoring pointless rules | cs.AI | The goal of inductive logic programming (ILP) is to find a set of logical
rules that generalises training examples and background knowledge. We introduce
an ILP approach that identifies pointless rules. A rule is pointless if it
contains a redundant literal or cannot discriminate against negative examples.
We show th... |
2502.01235 | One-step full gradient suffices for low-rank fine-tuning, provably and
efficiently | stat.ML cs.AI cs.LG | This paper studies how to improve the performance of Low-Rank Adaption (LoRA)
as guided by our theoretical analysis. Our first set of theoretical results
show that for random initialization and linear models, \textit{i)} LoRA will
align to the certain singular subspace of one-step gradient of full
fine-tuning; \texti... |
2502.01236 | Eliciting Language Model Behaviors with Investigator Agents | cs.LG cs.AI cs.CL | Language models exhibit complex, diverse behaviors when prompted with
free-form text, making it difficult to characterize the space of possible
outputs. We study the problem of behavior elicitation, where the goal is to
search for prompts that induce specific target behaviors (e.g., hallucinations
or harmful response... |
2502.01237 | The Differences Between Direct Alignment Algorithms are a Blur | cs.LG | Direct Alignment Algorithms (DAAs) simplify language model alignment by
replacing reinforcement learning (RL) and reward modeling (RM) in Reinforcement
Learning from Human Feedback (RLHF) with direct policy optimization. DAAs can
be classified by their ranking losses (pairwise vs. pointwise), by the rewards
used in t... |
2502.01242 | Neural Cellular Automata for Decentralized Sensing using a Soft
Inductive Sensor Array for Distributed Manipulator Systems | cs.RO cs.LG | In Distributed Manipulator Systems (DMS), decentralization is a highly
desirable property as it promotes robustness and facilitates scalability by
distributing computational burden and eliminating singular points of failure.
However, current DMS typically utilize a centralized approach to sensing, such
as single-came... |
2502.01243 | OphthBench: A Comprehensive Benchmark for Evaluating Large Language
Models in Chinese Ophthalmology | cs.CL cs.AI | Large language models (LLMs) have shown significant promise across various
medical applications, with ophthalmology being a notable area of focus. Many
ophthalmic tasks have shown substantial improvement through the integration of
LLMs. However, before these models can be widely adopted in clinical practice,
evaluati... |
2502.01247 | Learnable polynomial, trigonometric, and tropical activations | cs.LG cs.AI cs.CL cs.CV math.AG | This paper investigates scalable neural networks with learnable activation
functions based on orthogonal function bases and tropical polynomials,
targeting ImageNet-1K classification and next token prediction on OpenWebText.
Traditional activations, such as ReLU, are static. In contrast, learnable
activations enable ... |
2502.01248 | Computational modelling of cancer nanomedicine: Integrating hyperthermia
treatment into a multiphase porous-media tumour model | cs.CE | Heat-based cancer treatment, so-called hyperthermia, can be used to destroy
tumour cells directly or to make them more susceptible to chemotherapy or
radiation therapy. To apply heat locally, iron oxide nanoparticles are injected
into the bloodstream and accumulate at the tumour site, where they generate
heat when ex... |
2502.01250 | Beyond Win Rates: A Clustering-Based Approach to Character Balance
Analysis in Team-Based Games | cs.LG | Character diversity in competitive games, while enriching gameplay, often
introduces balance challenges that can negatively impact player experience and
strategic depth. Traditional balance assessments rely on aggregate metrics like
win rates and pick rates, which offer limited insight into the intricate
dynamics of ... |
2502.01253 | Explainability-Driven Quality Assessment for Rule-Based Systems | cs.AI cs.LO | This paper introduces an explanation framework designed to enhance the
quality of rules in knowledge-based reasoning systems based on dataset-driven
insights. The traditional method for rule induction from data typically
requires labor-intensive labeling and data-driven learning. This framework
provides an alternativ... |
2502.01256 | Soft is Safe: Human-Robot Interaction for Soft Robots | cs.RO | With the presence of robots increasing in the society, the need for
interacting with robots is becoming necessary. The field of Human-Robot
Interaction (HRI) has emerged important since more repetitive and tiresome jobs
are being done by robots. In the recent times, the field of soft robotics has
seen a boom in the f... |
2502.01262 | FSPGD: Rethinking Black-box Attacks on Semantic Segmentation | cs.CV | Transferability, the ability of adversarial examples crafted for one model to
deceive other models, is crucial for black-box attacks. Despite advancements in
attack methods for semantic segmentation, transferability remains limited,
reducing their effectiveness in real-world applications. To address this, we
introduc... |
2502.01264 | Generalized Lanczos method for systematic optimization of neural-network
quantum states | cond-mat.str-el cs.LG physics.comp-ph | Recently, artificial intelligence for science has made significant inroads
into various fields of natural science research. In the field of quantum
many-body computation, researchers have developed numerous ground state solvers
based on neural-network quantum states (NQSs), achieving ground state energies
with accura... |
2502.01265 | On Exact Learning of $d$-Monotone Functions | cs.LG cs.DS | In this paper, we study the learnability of the Boolean class of $d$-monotone
functions $f:{\cal X}\to\{0,1\}$ from membership and equivalence queries, where
$({\cal X},\le)$ is a finite lattice. We show that the class of $d$-monotone
functions that are represented in the form $f=F(g_1,g_2,\ldots,g_d)$, where $F$
is ... |
2502.01267 | Counterfactual Situation Testing: From Single to Multidimensional
Discrimination | cs.LG | We present counterfactual situation testing (CST), a causal data mining
framework for detecting individual discrimination in a dataset of classifier
decisions. CST answers the question "what would have been the model outcome had
the individual, or complainant, been of a different protected status?" It
extends the leg... |
2502.01268 | Resilient UAV Trajectory Planning via Few-Shot Meta-Offline
Reinforcement Learning | cs.RO cs.AI | Reinforcement learning (RL) has been a promising essence in future 5G-beyond
and 6G systems. Its main advantage lies in its robust model-free
decision-making in complex and large-dimension wireless environments. However,
most existing RL frameworks rely on online interaction with the environment,
which might not be f... |
2502.01269 | Exploratory Utility Maximization Problem with Tsallis Entropy | cs.LG q-fin.MF | We study expected utility maximization problem with constant relative risk
aversion utility function in a complete market under the reinforcement learning
framework. To induce exploration, we introduce the Tsallis entropy regularizer,
which generalizes the commonly used Shannon entropy. Unlike the classical
Merton's ... |
2502.01270 | Main Predicate and Their Arguments as Explanation Signals For Intent
Classification | cs.CL | Intent classification is crucial for conversational agents (chatbots), and
deep learning models perform well in this area. However, little research has
been done on the explainability of intent classification due to the absence of
suitable benchmark data. Human annotation of explanation signals in text
samples is tim... |
2502.01272 | Boosting Graph Robustness Against Backdoor Attacks: An Over-Similarity
Perspective | cs.LG | Graph Neural Networks (GNNs) have achieved notable success in tasks such as
social and transportation networks. However, recent studies have highlighted
the vulnerability of GNNs to backdoor attacks, raising significant concerns
about their reliability in real-world applications. Despite initial efforts to
defend aga... |
2502.01273 | Analysis of Student-LLM Interaction in a Software Engineering Project | cs.SE cs.AI | Large Language Models (LLMs) are becoming increasingly competent across
various domains, educators are showing a growing interest in integrating these
LLMs into the learning process. Especially in software engineering, LLMs have
demonstrated qualitatively better capabilities in code summarization, code
generation, an... |
2502.01276 | HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance | cs.LG cs.AI stat.ML | Hyperparameter optimization (HPO) is a crucial step in achieving strong
predictive performance. However, the impact of individual hyperparameters on
model generalization is highly context-dependent, prohibiting a
one-size-fits-all solution and requiring opaque automated machine learning
(AutoML) systems to find optim... |
2502.01278 | DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for
Mobile Users | eess.SP cs.LG | With the emergence of AI technologies in next-generation communication
systems, machine learning plays a pivotal role due to its ability to address
high-dimensional, non-stationary optimization problems within dynamic
environments while maintaining computational efficiency. One such application
is directional beamfor... |
2502.01280 | Trajectory Map-Matching in Urban Road Networks Based on RSS Measurements | eess.SY cs.SY | This paper proposes an RSS-based approach to reconstruct vehicle trajectories
within a road network, enforcing signal propagation rules and vehicle mobility
constraints to mitigate the impact of RSS noise and sparsity. The key challenge
lies in leveraging latent spatiotemporal correlations within RSS data while
navig... |
2502.01281 | Label Correction for Road Segmentation Using Road-side Cameras | cs.CV | Reliable road segmentation in all weather conditions is critical for
intelligent transportation applications, autonomous vehicles and advanced
driver's assistance systems. For robust performance, all weather conditions
should be included in the training data of deep learning-based perception
models. However, collecti... |
2502.01282 | Rational Gaussian wavelets and corresponding model driven neural
networks | stat.ML cs.AI cs.LG | In this paper we consider the continuous wavelet transform using Gaussian
wavelets multiplied by an appropriate rational term. The zeros and poles of
this rational modifier act as free parameters and their choice highly
influences the shape of the mother wavelet. This allows the proposed
construction to approximate s... |
2502.01286 | Template Matching in Images using Segmented Normalized Cross-Correlation | cs.CV | In this paper, a new variant of an algorithm for normalized cross-correlation
(NCC) is proposed in the context of template matching in images. The proposed
algorithm is based on the precomputation of a template image approximation,
enabling more efficient calculation of approximate NCC with the source image
than usin... |
2502.01289 | A Framework for Double-Blind Federated Adaptation of Foundation Models | cs.LG cs.CR cs.CV cs.DC | The availability of foundational models (FMs) pre-trained on large-scale data
has advanced the state-of-the-art in many computer vision tasks. While FMs have
demonstrated good zero-shot performance on many image classification tasks,
there is often scope for performance improvement by adapting the FM to the
downstrea... |
2502.01295 | Common Foundations for SHACL, ShEx, and PG-Schema | cs.DB cs.AI | Graphs have emerged as an important foundation for a variety of applications,
including capturing and reasoning over factual knowledge, semantic data
integration, social networks, and providing factual knowledge for machine
learning algorithms. To formalise certain properties of the data and to ensure
data quality, t... |
2502.01296 | Molecular Odor Prediction with Harmonic Modulated Feature Mapping and
Chemically-Informed Loss | cs.LG q-bio.QM | Molecular odor prediction has great potential across diverse fields such as
chemistry, pharmaceuticals, and environmental science, enabling the rapid
design of new materials and enhancing environmental monitoring. However,
current methods face two main challenges: First, existing models struggle with
non-smooth objec... |
2502.01297 | XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization
for XR Applications | cs.CV | This paper presents a novel approach to Visual Inertial Odometry (VIO),
focusing on the initialization and feature matching modules. Existing methods
for initialization often suffer from either poor stability in visual Structure
from Motion (SfM) or fragility in solving a huge number of parameters
simultaneously. To ... |
2502.01298 | Augmented Knowledge Graph Querying leveraging LLMs | cs.IR | Adopting Knowledge Graphs (KGs) as a structured, semantic-oriented, data
representation model has significantly improved data integration, reasoning,
and querying capabilities across different domains. This is especially true in
modern scenarios such as Industry 5.0, in which the integration of data
produced by human... |
2502.01299 | Probabilistic adaptation of language comprehension for individual
speakers: Evidence from neural oscillations | q-bio.NC cs.CL | Listeners adapt language comprehension based on their mental representations
of speakers, but how these representations are dynamically updated remains
unclear. We investigated whether listeners probabilistically adapt their
comprehension based on the likelihood of speakers producing
stereotype-incongruent utterances... |
2502.01303 | Partial Channel Network: Compute Fewer, Perform Better | cs.CV cs.AI | Designing a module or mechanism that enables a network to maintain low
parameters and FLOPs without sacrificing accuracy and throughput remains a
challenge. To address this challenge and exploit the redundancy within feature
map channels, we propose a new solution: partial channel mechanism (PCM).
Specifically, throu... |
2502.01304 | Towards Autonomous Wood-Log Grasping with a Forestry Crane: Simulator
and Benchmarking | cs.RO | Forestry machines operated in forest production environments face challenges
when performing manipulation tasks, especially regarding the complicated
dynamics of underactuated crane systems and the heavy weight of logs to be
grasped. This study investigates the feasibility of using reinforcement
learning for forestry... |
2502.01307 | Improving the Effectiveness of Potential-Based Reward Shaping in
Reinforcement Learning | cs.LG | Potential-based reward shaping is commonly used to incorporate prior
knowledge of how to solve the task into reinforcement learning because it can
formally guarantee policy invariance. As such, the optimal policy and the
ordering of policies by their returns are not altered by potential-based reward
shaping. In this ... |
2502.01309 | Heterogeneous Image GNN: Graph-Conditioned Diffusion for Image Synthesis | cs.CV | We introduce a novel method for conditioning diffusion-based image synthesis
models with heterogeneous graph data. Existing approaches typically incorporate
conditioning variables directly into model architectures, either through
cross-attention layers that attend to text latents or image concatenation that
spatially... |
2502.01310 | A Statistical Learning Perspective on Semi-dual Adversarial Neural
Optimal Transport Solvers | cs.LG cs.AI | Neural network based Optimal Transport (OT) is a recent and fruitful
direction in the generative modeling community. It finds its applications in
various fields such as domain translation, image super-resolution,
computational biology and others. Among the existing approaches to OT, of
considerable interest are adver... |
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