id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
2502.12902 | Probabilistic neural operators for functional uncertainty quantification | cs.LG | Neural operators aim to approximate the solution operator of a system of
differential equations purely from data. They have shown immense success in
modeling complex dynamical systems across various domains. However, the
occurrence of uncertainties inherent in both model and data has so far rarely
been taken into acc... |
2502.12904 | Fraud-R1 : A Multi-Round Benchmark for Assessing the Robustness of LLM
Against Augmented Fraud and Phishing Inducements | cs.CL | We introduce Fraud-R1, a benchmark designed to evaluate LLMs' ability to
defend against internet fraud and phishing in dynamic, real-world scenarios.
Fraud-R1 comprises 8,564 fraud cases sourced from phishing scams, fake job
postings, social media, and news, categorized into 5 major fraud types. Unlike
previous bench... |
2502.12908 | Graph Neural Networks for Databases: A Survey | cs.DB cs.AI | Graph neural networks (GNNs) are powerful deep learning models for
graph-structured data, demonstrating remarkable success across diverse domains.
Recently, the database (DB) community has increasingly recognized the
potentiality of GNNs, prompting a surge of researches focusing on improving
database systems through ... |
2502.12911 | Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL
Generation | cs.CL cs.DB | Generating SQLs from user queries is a long-standing challenge, where the
accuracy of initial schema linking significantly impacts subsequent SQL
generation performance. However, current schema linking models still struggle
with missing relevant schema elements or an excess of redundant ones. A crucial
reason for thi... |
2502.12912 | A Simplified and Numerically Stable Approach to the BG/NBD Churn
Prediction model | stat.OT cs.LG math.ST stat.TH | This study extends the BG/NBD churn probability model, addressing its
limitations in industries where customer behaviour is often influenced by
seasonal events and possibly high purchase counts. We propose a modified
definition of churn, considering a customer to have churned if they make no
purchases within M days. ... |
2502.12913 | GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training
for LLMs On-Device Fine-tuning | cs.LG cs.AI cs.CL | Large Language Models (LLMs) fine-tuning technologies have achieved
remarkable results. However, traditional LLM fine-tuning approaches face
significant challenges: they require large Floating Point (FP) computation,
raising privacy concerns when handling sensitive data, and are impractical for
resource-constrained e... |
2502.12917 | Contrast-Unity for Partially-Supervised Temporal Sentence Grounding | cs.CV | Temporal sentence grounding aims to detect event timestamps described by the
natural language query from given untrimmed videos. The existing
fully-supervised setting achieves great results but requires expensive
annotation costs; while the weakly-supervised setting adopts cheap labels but
performs poorly. To pursue ... |
2502.12918 | Query Rewriting via LLMs | cs.DB | Query rewriting is a classical technique for transforming complex declarative
SQL queries into ``lean'' equivalents that are conducive to (a) faster
execution from a performance perspective, and (b) better understanding from a
developer perspective. The rewriting is typically achieved via transformation
rules, but th... |
2502.12919 | A Smooth Transition Between Induction and Deduction: Fast Abductive
Learning Based on Probabilistic Symbol Perception | cs.LG | Abductive learning (ABL) that integrates strengths of machine learning and
logical reasoning to improve the learning generalization, has been recently
shown effective. However, its efficiency is affected by the transition between
numerical induction and symbolical deduction, leading to high computational
costs in the... |
2502.12920 | Lightweight Online Adaption for Time Series Foundation Model Forecasts | cs.LG stat.ML | Foundation models (FMs) have emerged as a promising approach for time series
forecasting. While effective, FMs typically remain fixed during deployment due
to the high computational costs of learning them online. Consequently, deployed
FMs fail to adapt their forecasts to current data characteristics, despite the
ava... |
2502.12921 | Q-STRUM Debate: Query-Driven Contrastive Summarization for
Recommendation Comparison | cs.CL | Query-driven recommendation with unknown items poses a challenge for users to
understand why certain items are appropriate for their needs. Query-driven
Contrastive Summarization (QCS) is a methodology designed to address this issue
by leveraging language-based item descriptions to clarify contrasts between
them. How... |
2502.12923 | On-Device LLMs for Home Assistant: Dual Role in Intent Detection and
Response Generation | cs.CL | This paper investigates whether Large Language Models (LLMs), fine-tuned on
synthetic but domain-representative data, can perform the twofold task of (i)
slot and intent detection and (ii) natural language response generation for a
smart home assistant, while running solely on resource-limited, CPU-only edge
hardware... |
2502.12924 | Conditioning LLMs to Generate Code-Switched Text: A Methodology Grounded
in Naturally Occurring Data | cs.CL cs.AI | Code-switching (CS) is still a critical challenge in Natural Language
Processing (NLP). Current Large Language Models (LLMs) struggle to interpret
and generate code-switched text, primarily due to the scarcity of large-scale
CS datasets for training. This paper presents a novel methodology to generate
CS data using L... |
2502.12925 | Keep what you need : extracting efficient subnetworks from large audio
representation models | cs.SD cs.AI | Recently, research on audio foundation models has witnessed notable advances,
as illustrated by the ever improving results on complex downstream tasks.
Subsequently, those pretrained networks have quickly been used for various
audio applications. These improvements have however resulted in a considerable
increase bot... |
2502.12926 | Towards more Contextual Agents: An extractor-Generator Optimization
Framework | cs.AI | Large Language Model (LLM)-based agents have demonstrated remarkable success
in solving complex tasks across a wide range of general-purpose applications.
However, their performance often degrades in context-specific scenarios, such
as specialized industries or research domains, where the absence of
domain-relevant k... |
2502.12927 | SEFL: Harnessing Large Language Model Agents to Improve Educational
Feedback Systems | cs.CL | Providing high-quality feedback is crucial for student success but is
constrained by time, cost, and limited data availability. We introduce
Synthetic Educational Feedback Loops (SEFL), a novel framework designed to
deliver immediate, on-demand feedback at scale without relying on extensive,
real-world student data. ... |
2502.12928 | Finedeep: Mitigating Sparse Activation in Dense LLMs via Multi-Layer
Fine-Grained Experts | cs.CL | Large language models have demonstrated exceptional performance across a wide
range of tasks. However, dense models usually suffer from sparse activation,
where many activation values tend towards zero (i.e., being inactivated). We
argue that this could restrict the efficient exploration of model
representation space... |
2502.12929 | Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking
Through Options | cs.LG cs.AI cs.CL | We present a novel reasoning approach called Flow-of-Options (FoO), designed
to address intrinsic biases in Large Language Models (LLMs). FoO enables LLMs
to systematically explore a diverse range of possibilities in their reasoning,
as demonstrated by an FoO-based agentic system for autonomously solving Machine
Lear... |
2502.12930 | Universal Embedding Function for Traffic Classification via QUIC Domain
Recognition Pretraining: A Transfer Learning Success | cs.LG cs.NI | Encrypted traffic classification (TC) methods must adapt to new protocols and
extensions as well as to advancements in other machine learning fields. In this
paper, we follow a transfer learning setup best known from computer vision. We
first pretrain an embedding model on a complex task with a large number of
classe... |
2502.12932 | Synthetic Data Generation for Culturally Nuanced Commonsense Reasoning
in Low-Resource Languages | cs.CL | Quantifying reasoning capability in low-resource languages remains a
challenge in NLP due to data scarcity and limited access to annotators. While
LLM-assisted dataset construction has proven useful for medium- and
high-resource languages, its effectiveness in low-resource languages,
particularly for commonsense reas... |
2502.12937 | Tuning Algorithmic and Architectural Hyperparameters in Graph-Based
Semi-Supervised Learning with Provable Guarantees | cs.LG | Graph-based semi-supervised learning is a powerful paradigm in machine
learning for modeling and exploiting the underlying graph structure that
captures the relationship between labeled and unlabeled data. A large number of
classical as well as modern deep learning based algorithms have been proposed
for this problem... |
2502.12944 | Performance of Zero-Shot Time Series Foundation Models on Cloud Data | cs.LG | Time series foundation models (FMs) have emerged as a popular paradigm for
zero-shot multi-domain forecasting. FMs are trained on numerous diverse
datasets and claim to be effective forecasters across multiple different time
series domains, including cloud data. In this work we investigate this claim,
exploring the e... |
2502.12945 | LLMPopcorn: An Empirical Study of LLMs as Assistants for Popular
Micro-video Generation | cs.CL cs.CV | Popular Micro-videos, dominant on platforms like TikTok and YouTube, hold
significant commercial value. The rise of high-quality AI-generated content has
spurred interest in AI-driven micro-video creation. However, despite the
advanced capabilities of large language models (LLMs) like ChatGPT and DeepSeek
in text gen... |
2502.12947 | Every Expert Matters: Towards Effective Knowledge Distillation for
Mixture-of-Experts Language Models | cs.CL cs.AI cs.LG | With the emergence of Mixture-of-Experts (MoE), the efficient scaling of
model size has accelerated the development of large language models in recent
years. However, their high memory requirements prevent their use in
resource-constrained environments. While knowledge distillation (KD) has been a
proven method for m... |
2502.12948 | Fake It Till You Make It: Using Synthetic Data and Domain Knowledge for
Improved Text-Based Learning for LGE Detection | cs.CV cs.AI | Detection of hyperenhancement from cardiac LGE MRI images is a complex task
requiring significant clinical expertise. Although deep learning-based models
have shown promising results for the task, they require large amounts of data
with fine-grained annotations. Clinical reports generated for cardiac MR
studies conta... |
2502.12949 | Efficient Learning Under Density Shift in Incremental Settings Using
Cram\'er-Rao-Based Regularization | cs.LG stat.ML | The continuous surge in data volume and velocity is often dealt with using
data orchestration and distributed processing approaches, abstracting away the
machine learning challenges that exist at the algorithmic level. With growing
interest in automating the learning loop, training with data that arrive in a
sequence... |
2502.12950 | Towards Hybrid Traffic Laws for Mixed Flow of Human-Driven Vehicles and
Connected Autonomous Vehicles | cs.MA | Hybrid traffic laws represent an innovative approach to managing mixed
environments of connected autonomous vehicles (CAVs) and human-driven vehicles
(HDVs) by introducing separate sets of regulations for each vehicle type. These
laws are designed to leverage the unique capabilities of CAVs while ensuring
both types ... |
2502.12951 | Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific
Data Compression | cs.LG | This paper proposes a new compression paradigm -- Guaranteed Conditional
Diffusion with Tensor Correction (GCDTC) -- for lossy scientific data
compression. The framework is based on recent conditional diffusion (CD)
generative models, and it consists of a conditional diffusion model, tensor
correction, and error guar... |
2502.12953 | Task-Informed Anti-Curriculum by Masking Improves Downstream Performance
on Text | cs.CL cs.AI cs.LG | Masked language modeling has become a widely adopted unsupervised technique
to pre-train language models. However, the process of selecting tokens for
masking is random, and the percentage of masked tokens is typically fixed for
the entire training process. In this paper, we propose to adjust the masking
ratio and to... |
2502.12958 | Preventing the Popular Item Embedding Based Attack in Federated
Recommendations | cs.CR cs.DB cs.LG | Privacy concerns have led to the rise of federated recommender systems (FRS),
which can create personalized models across distributed clients. However, FRS
is vulnerable to poisoning attacks, where malicious users manipulate gradients
to promote their target items intentionally. Existing attacks against FRS have
limi... |
2502.12959 | AlignFreeze: Navigating the Impact of Realignment on the Layers of
Multilingual Models Across Diverse Languages | cs.CL cs.AI | Realignment techniques are often employed to enhance cross-lingual transfer
in multilingual language models, still, they can sometimes degrade performance
in languages that differ significantly from the fine-tuned source language.
This paper introduces AlignFreeze, a method that freezes either the layers'
lower half ... |
2502.12961 | Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger | cs.AI cs.CL | Large language models (LLMs) have shown remarkable emergent capabilities,
transforming the execution of functional tasks by leveraging external tools for
complex problems that require specialized processing or real-time data. While
existing research expands LLMs access to diverse tools (e.g., program
interpreters, se... |
2502.12962 | Infinite Retrieval: Attention Enhanced LLMs in Long-Context Processing | cs.CL | Limited by the context window size of Large Language Models(LLMs), handling
various tasks with input tokens exceeding the upper limit has been challenging,
whether it is a simple direct retrieval task or a complex multi-hop reasoning
task. Although various methods have been proposed to enhance the long-context
proces... |
2502.12963 | D3-ARM: High-Dynamic, Dexterous and Fully Decoupled Cable-driven Robotic
Arm | cs.RO | Cable transmission enables motors of robotic arm to operate lightweight and
low-inertia joints remotely in various environments, but it also creates issues
with motion coupling and cable routing that can reduce arm's control precision
and performance. In this paper, we present a novel motion decoupling mechanism
with... |
2502.12964 | Trust Me, I'm Wrong: High-Certainty Hallucinations in LLMs | cs.CL | Large Language Models (LLMs) often generate outputs that lack grounding in
real-world facts, a phenomenon known as hallucinations. Prior research has
associated hallucinations with model uncertainty, leveraging this relationship
for hallucination detection and mitigation. In this paper, we challenge the
underlying as... |
2502.12965 | A Survey of Text Classification Under Class Distribution Shift | cs.CL cs.AI cs.LG | The basic underlying assumption of machine learning (ML) models is that the
training and test data are sampled from the same distribution. However, in
daily practice, this assumption is often broken, i.e.~the distribution of the
test data changes over time, which hinders the application of conventional ML
models. One... |
2502.12966 | The Early Days of the Ethereum Blob Fee Market and Lessons Learnt | cs.CE cs.CR cs.DC cs.ET econ.GN q-fin.EC | Ethereum has adopted a rollup-centric roadmap to scale by making rollups
(layer 2 scaling solutions) the primary method for handling transactions. The
first significant step towards this goal was EIP-4844, which introduced blob
transactions that are designed to meet the data availability needs of layer 2
protocols. T... |
2502.12970 | Reasoning-to-Defend: Safety-Aware Reasoning Can Defend Large Language
Models from Jailbreaking | cs.CL | The reasoning abilities of Large Language Models (LLMs) have demonstrated
remarkable advancement and exceptional performance across diverse domains.
However, leveraging these reasoning capabilities to enhance LLM safety against
adversarial attacks and jailbreak queries remains largely unexplored. To bridge
this gap, ... |
2502.12973 | Optimizing Social Network Interventions via Hypergradient-Based
Recommender System Design | cs.SI cs.SY eess.SY math.OC | Although social networks have expanded the range of ideas and information
accessible to users, they are also criticized for amplifying the polarization
of user opinions. Given the inherent complexity of these phenomena, existing
approaches to counteract these effects typically rely on handcrafted algorithms
and heuri... |
2502.12974 | Learning More Effective Representations for Dense Retrieval through
Deliberate Thinking Before Search | cs.IR | Recent dense retrievers usually thrive on the emergency capabilities of Large
Language Models (LLMs), using them to encode queries and documents into an
embedding space for retrieval. These LLM-based dense retrievers have shown
promising performance across various retrieval scenarios. However, relying on a
single emb... |
2502.12975 | Instance-Level Moving Object Segmentation from a Single Image with
Events | cs.CV | Moving object segmentation plays a crucial role in understanding dynamic
scenes involving multiple moving objects, while the difficulties lie in taking
into account both spatial texture structures and temporal motion cues. Existing
methods based on video frames encounter difficulties in distinguishing whether
pixel d... |
2502.12976 | Does Training with Synthetic Data Truly Protect Privacy? | cs.CR cs.LG | As synthetic data becomes increasingly popular in machine learning tasks,
numerous methods--without formal differential privacy guarantees--use synthetic
data for training. These methods often claim, either explicitly or implicitly,
to protect the privacy of the original training data. In this work, we explore
four d... |
2502.12977 | Time-series attribution maps with regularized contrastive learning | stat.ML cs.AI cs.LG q-bio.NC | Gradient-based attribution methods aim to explain decisions of deep learning
models but so far lack identifiability guarantees. Here, we propose a method to
generate attribution maps with identifiability guarantees by developing a
regularized contrastive learning algorithm trained on time-series data plus a
new attri... |
2502.12978 | Statistically Significant $k$NNAD by Selective Inference | stat.ML cs.LG | In this paper, we investigate the problem of unsupervised anomaly detection
using the k-Nearest Neighbor method. The k-Nearest Neighbor Anomaly Detection
(kNNAD) is a simple yet effective approach for identifying anomalies across
various domains and fields. A critical challenge in anomaly detection,
including kNNAD, ... |
2502.12979 | Electron flow matching for generative reaction mechanism prediction
obeying conservation laws | cs.LG | Central to our understanding of chemical reactivity is the principle of mass
conservation, which is fundamental for ensuring physical consistency, balancing
equations, and guiding reaction design. However, data-driven computational
models for tasks such as reaction product prediction rarely abide by this most
basic c... |
2502.12981 | Towards Variational Flow Matching on General Geometries | cs.LG math.DG | We introduce Riemannian Gaussian Variational Flow Matching (RG-VFM), an
extension of Variational Flow Matching (VFM) that leverages Riemannian Gaussian
distributions for generative modeling on structured manifolds. We derive a
variational objective for probability flows on manifolds with closed-form
geodesics, making... |
2502.12982 | Sailor2: Sailing in South-East Asia with Inclusive Multilingual LLMs | cs.CL cs.AI cs.LG | Sailor2 is a family of cutting-edge multilingual language models for
South-East Asian (SEA) languages, available in 1B, 8B, and 20B sizes to suit
diverse applications. Building on Qwen2.5, Sailor2 undergoes continuous
pre-training on 500B tokens (400B SEA-specific and 100B replay tokens) to
support 13 SEA languages w... |
2502.12984 | On Erlang mixture approximations for differential equations with
distributed time delays | math.DS cs.NA cs.SY eess.SY math.NA | In this paper, we propose a general approach for approximate simulation and
analysis of delay differential equations (DDEs) with distributed time delays
based on methods for ordinary differential equations (ODEs). The key innovation
is that we 1) approximate the kernel by the probability density function of an
Erlang... |
2502.12985 | PartSDF: Part-Based Implicit Neural Representation for Composite 3D
Shape Parametrization and Optimization | cs.CV cs.AI | Accurate 3D shape representation is essential in engineering applications
such as design, optimization, and simulation. In practice, engineering
workflows require structured, part-aware representations, as objects are
inherently designed as assemblies of distinct components. However, most
existing methods either mode... |
2502.12987 | Ensemble Kalman filter in latent space using a variational autoencoder
pair | cs.LG physics.ao-ph | Popular (ensemble) Kalman filter data assimilation (DA) approaches assume
that the errors in both the a priori estimate of the state and those in the
observations are Gaussian. For constrained variables, e.g. sea ice
concentration or stress, such an assumption does not hold. The variational
autoencoder (VAE) is a mac... |
2502.12988 | Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in
LLMs | cs.CL | Previous approaches to persona simulation large language models (LLMs) have
typically relied on learning basic biographical information, or using limited
role-play dialogue datasets to capture a character's responses. However, a
holistic representation of an individual goes beyond surface-level facts or
conversations... |
2502.12992 | B-cos LM: Efficiently Transforming Pre-trained Language Models for
Improved Explainability | cs.CL cs.AI | Post-hoc explanation methods for black-box models often struggle with
faithfulness and human interpretability due to the lack of explainability in
current neural models. Meanwhile, B-cos networks have been introduced to
improve model explainability through architectural and computational
adaptations, but their applic... |
2502.12993 | Approximate Tree Completion and Learning-Augmented Algorithms for Metric
Minimum Spanning Trees | cs.DS cs.DM cs.LG | Finding a minimum spanning tree (MST) for $n$ points in an arbitrary metric
space is a fundamental primitive for hierarchical clustering and many other ML
tasks, but this takes $\Omega(n^2)$ time to even approximate. We introduce a
framework for metric MSTs that first (1) finds a forest of disconnected
components usi... |
2502.12994 | SHADeS: Self-supervised Monocular Depth Estimation Through
Non-Lambertian Image Decomposition | cs.CV | Purpose: Visual 3D scene reconstruction can support colonoscopy navigation.
It can help in recognising which portions of the colon have been visualised and
characterising the size and shape of polyps. This is still a very challenging
problem due to complex illumination variations, including abundant specular
reflecti... |
2502.12995 | Free Argumentative Exchanges for Explaining Image Classifiers | cs.AI | Deep learning models are powerful image classifiers but their opacity hinders
their trustworthiness. Explanation methods for capturing the reasoning process
within these classifiers faithfully and in a clear manner are scarce, due to
their sheer complexity and size. We provide a solution for this problem by
defining ... |
2502.12996 | Eager Updates For Overlapped Communication and Computation in DiLoCo | cs.CL | Distributed optimization methods such as DiLoCo have been shown to be
effective in training very large models across multiple distributed workers,
such as datacenters. These methods split updates into two parts: an inner
optimization phase, where the workers independently execute multiple
optimization steps on their ... |
2502.12998 | Personalized Top-k Set Queries Over Predicted Scores | cs.DB cs.AI cs.LG | This work studies the applicability of expensive external oracles such as
large language models in answering top-k queries over predicted scores. Such
scores are incurred by user-defined functions to answer personalized queries
over multi-modal data. We propose a generic computational framework that
handles arbitrary... |
2502.12999 | Asymptotic Optimism of Random-Design Linear and Kernel Regression Models | stat.ML cs.LG math.ST stat.TH | We derived the closed-form asymptotic optimism of linear regression models
under random designs, and generalizes it to kernel ridge regression. Using
scaled asymptotic optimism as a generic predictive model complexity measure, we
studied the fundamental different behaviors of linear regression model, tangent
kernel (... |
2502.13000 | Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied
Edges | cs.DS cs.DM cs.LG | We consider a framework for clustering edge-colored hypergraphs, where the
goal is to cluster (equivalently, to color) objects based on the primary type
of multiway interactions they participate in. One well-studied objective is to
color nodes to minimize the number of unsatisfied hyperedges -- those
containing one o... |
2502.13001 | You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with
a Multi-Agent Conversations | cs.AI cs.CL | Meeting summarization suffers from limited high-quality data, mainly due to
privacy restrictions and expensive collection processes. We address this gap
with FAME, a dataset of 500 meetings in English and 300 in German produced by
MIMIC, our new multi-agent meeting synthesis framework that generates meeting
transcrip... |
2502.13004 | Language Barriers: Evaluating Cross-Lingual Performance of CNN and
Transformer Architectures for Speech Quality Estimation | cs.CL | Objective speech quality models aim to predict human-perceived speech quality
using automated methods. However, cross-lingual generalization remains a major
challenge, as Mean Opinion Scores (MOS) vary across languages due to
linguistic, perceptual, and dataset-specific differences. A model trained
primarily on Engli... |
2502.13006 | Integrating Reinforcement Learning, Action Model Learning, and Numeric
Planning for Tackling Complex Tasks | cs.AI | Automated Planning algorithms require a model of the domain that specifies
the preconditions and effects of each action. Obtaining such a domain model is
notoriously hard. Algorithms for learning domain models exist, yet it remains
unclear whether learning a domain model and planning is an effective approach
for nume... |
2502.13010 | Adaptive Knowledge Graphs Enhance Medical Question Answering: Bridging
the Gap Between LLMs and Evolving Medical Knowledge | cs.CL cs.MA | Large Language Models (LLMs) have significantly advanced medical
question-answering by leveraging extensive clinical data and medical
literature. However, the rapid evolution of medical knowledge and the
labor-intensive process of manually updating domain-specific resources pose
challenges to the reliability of these... |
2502.13012 | Towards a Design Guideline for RPA Evaluation: A Survey of Large
Language Model-Based Role-Playing Agents | cs.HC cs.CL | Role-Playing Agent (RPA) is an increasingly popular type of LLM Agent that
simulates human-like behaviors in a variety of tasks. However, evaluating RPAs
is challenging due to diverse task requirements and agent designs. This paper
proposes an evidence-based, actionable, and generalizable evaluation design
guideline ... |
2502.13013 | HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit | cs.RO cs.AI cs.HC | Current humanoid teleoperation systems either lack reliable low-level control
policies, or struggle to acquire accurate whole-body control commands, making
it difficult to teleoperate humanoids for loco-manipulation tasks. To solve
these issues, we propose HOMIE, a novel humanoid teleoperation cockpit
integrates a hu... |
2502.13016 | LLM-Powered Proactive Data Systems | cs.DB cs.AI | With the power of LLMs, we now have the ability to query data that was
previously impossible to query, including text, images, and video. However,
despite this enormous potential, most present-day data systems that leverage
LLMs are reactive, reflecting our community's desire to map LLMs to known
abstractions. Most d... |
2502.13017 | Mean of Means: Human Localization with Calibration-free and
Unconstrained Camera Settings (extended version) | cs.CV cs.GR | Accurate human localization is crucial for various applications, especially
in the Metaverse era. Existing high precision solutions rely on expensive,
tag-dependent hardware, while vision-based methods offer a cheaper, tag-free
alternative. However, current vision solutions based on stereo vision face
limitations due... |
2502.13019 | Oreo: A Plug-in Context Reconstructor to Enhance Retrieval-Augmented
Generation | cs.CL | Despite the remarkable capabilities of Large Language Models (LLMs) in
various NLP tasks, they remain vulnerable to hallucinations due to their
limited parametric knowledge and lack of domain-specific expertise.
Retrieval-Augmented Generation (RAG) addresses this challenge by incorporating
external document retrieval... |
2502.13022 | Efficient and Sharp Off-Policy Learning under Unobserved Confounding | cs.LG | We develop a novel method for personalized off-policy learning in scenarios
with unobserved confounding. Thereby, we address a key limitation of standard
policy learning: standard policy learning assumes unconfoundedness, meaning
that no unobserved factors influence both treatment assignment and outcomes.
However, th... |
2502.13023 | Detection and Geographic Localization of Natural Objects in the Wild: A
Case Study on Palms | cs.CV cs.LG | Palms are ecologically and economically indicators of tropical forest health,
biodiversity, and human impact that support local economies and global forest
product supply chains. While palm detection in plantations is well-studied,
efforts to map naturally occurring palms in dense forests remain limited by
overlappin... |
2502.13024 | Fragility-aware Classification for Understanding Risk and Improving
Generalization | cs.LG math.OC | Classification models play a critical role in data-driven decision-making
applications such as medical diagnosis, user profiling, recommendation systems,
and default detection. Traditional performance metrics, such as accuracy, focus
on overall error rates but fail to account for the confidence of incorrect
predictio... |
2502.13025 | Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks | cs.AI cond-mat.mtrl-sci cs.CL cs.LG | We present an agentic, autonomous graph expansion framework that iteratively
structures and refines knowledge in situ. Unlike conventional knowledge graph
construction methods relying on static extraction or single-pass learning, our
approach couples a reasoning-native large language model with a continually
updated ... |
2502.13027 | A deep learning framework for efficient pathology image analysis | cs.CV | Artificial intelligence (AI) has transformed digital pathology by enabling
biomarker prediction from high-resolution whole slide images (WSIs). However,
current methods are computationally inefficient, processing thousands of
redundant tiles per WSI and requiring complex aggregator models. We introduce
EAGLE (Efficie... |
2502.13028 | Whose story is it? Personalizing story generation by inferring author
styles | cs.CL | Personalization has become essential for improving user experience in
interactive writing and educational applications, yet its potential in story
generation remains largely unexplored. In this work, we propose a novel
two-stage pipeline for personalized story generation. Our approach first infers
an author's implici... |
2502.13030 | Likelihood-Ratio Regularized Quantile Regression: Adapting Conformal
Prediction to High-Dimensional Covariate Shifts | stat.ML cs.AI cs.LG | We consider the problem of conformal prediction under covariate shift. Given
labeled data from a source domain and unlabeled data from a covariate shifted
target domain, we seek to construct prediction sets with valid marginal
coverage in the target domain. Most existing methods require estimating the
unknown likelih... |
2502.13031 | HPSS: Heuristic Prompting Strategy Search for LLM Evaluators | cs.CL | Since the adoption of large language models (LLMs) for text evaluation has
become increasingly prevalent in the field of natural language processing
(NLP), a series of existing works attempt to optimize the prompts for LLM
evaluators to improve their alignment with human judgment. However, their
efforts are limited t... |
2502.13034 | Natural Language Generation from Visual Sequences: Challenges and Future
Directions | cs.CL cs.AI cs.CV cs.LG | The ability to use natural language to talk about visual content is at the
core of human intelligence and a crucial feature of any artificial intelligence
system. Various studies have focused on generating text for single images. In
contrast, comparatively little attention has been paid to exhaustively
analyzing and ... |
2502.13037 | Enhancing Power Grid Inspections with Machine Learning | cs.CV | Ensuring the safety and reliability of power grids is critical as global
energy demands continue to rise. Traditional inspection methods, such as manual
observations or helicopter surveys, are resource-intensive and lack
scalability. This paper explores the use of 3D computer vision to automate
power grid inspections... |
2502.13042 | Network-Realized Model Predictive Control Part I: NRF-Enabled
Closed-loop Decomposition | eess.SY cs.SY | A two-layer control architecture is proposed, which promotes scalable
implementations for model predictive controllers. The top layer acts as both
reference governor for the bottom layer, and as a feedback controller for the
regulated network. By employing set-based methods, global theoretical
guarantees are obtained... |
2502.13044 | Do we still need Human Annotators? Prompting Large Language Models for
Aspect Sentiment Quad Prediction | cs.CL | Aspect sentiment quadruple prediction (ASQP) facilitates a detailed
understanding of opinions expressed in a text by identifying the opinion term,
aspect term, aspect category and sentiment polarity for each opinion. However,
annotating a full set of training examples to fine-tune models for ASQP is a
resource-intens... |
2502.13049 | $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering | cs.LG | Time series clustering poses a significant challenge with diverse
applications across domains. A prominent drawback of existing solutions lies in
their limited interpretability, often confined to presenting users with
centroids. In addressing this gap, our work presents $k$-Graph, an unsupervised
method explicitly cr... |
2502.13053 | AEIA-MN: Evaluating the Robustness of Multimodal LLM-Powered Mobile
Agents Against Active Environmental Injection Attacks | cs.CL | As researchers continuously optimize AI agents to perform tasks more
effectively within operating systems, they often neglect to address the
critical need for enabling these agents to identify "impostors" within the
system. Through an analysis of the agents' operating environment, we identified
a potential threat: at... |
2502.13055 | LAMD: Context-driven Android Malware Detection and Classification with
LLMs | cs.CR cs.AI cs.LG | The rapid growth of mobile applications has escalated Android malware
threats. Although there are numerous detection methods, they often struggle
with evolving attacks, dataset biases, and limited explainability. Large
Language Models (LLMs) offer a promising alternative with their zero-shot
inference and reasoning c... |
2502.13056 | Benchmarking MedMNIST dataset on real quantum hardware | quant-ph cs.LG | Quantum machine learning (QML) has emerged as a promising domain to leverage
the computational capabilities of quantum systems to solve complex
classification tasks. In this work, we present first comprehensive QML study by
benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a
127-qubit real... |
2502.13059 | SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large
Language Models | cs.CL | The increasing application of multi-modal large language models (MLLMs)
across various sectors have spotlighted the essence of their output reliability
and accuracy, particularly their ability to produce content grounded in factual
information (e.g. common and domain-specific knowledge). In this work, we
introduce Si... |
2502.13061 | Improved Fine-Tuning of Large Multimodal Models for Hateful Meme
Detection | cs.CL cs.AI cs.CV cs.LG | Hateful memes have become a significant concern on the Internet,
necessitating robust automated detection systems. While large multimodal models
have shown strong generalization across various tasks, they exhibit poor
generalization to hateful meme detection due to the dynamic nature of memes
tied to emerging social ... |
2502.13062 | AI-Assisted Decision Making with Human Learning | cs.AI cs.GT cs.HC | AI systems increasingly support human decision-making. In many cases, despite
the algorithm's superior performance, the final decision remains in human
hands. For example, an AI may assist doctors in determining which diagnostic
tests to run, but the doctor ultimately makes the diagnosis. This paper studies
such AI-a... |
2502.13063 | Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the
Limits of Embedding Space Capacity | cs.CL cs.LG | A range of recent works addresses the problem of compression of sequence of
tokens into a shorter sequence of real-valued vectors to be used as inputs
instead of token embeddings or key-value cache. These approaches allow to
reduce the amount of compute in existing language models. Despite relying on
powerful models ... |
2502.13069 | Interactive Agents to Overcome Ambiguity in Software Engineering | cs.AI | AI agents are increasingly being deployed to automate tasks, often based on
ambiguous and underspecified user instructions. Making unwarranted assumptions
and failing to ask clarifying questions can lead to suboptimal outcomes, safety
risks due to tool misuse, and wasted computational resources. In this work, we
stud... |
2502.13071 | RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye
View for 3D Object Detection | cs.CV | While recent low-cost radar-camera approaches have shown promising results in
multi-modal 3D object detection, both sensors face challenges from
environmental and intrinsic disturbances. Poor lighting or adverse weather
conditions degrade camera performance, while radar suffers from noise and
positional ambiguity. Ac... |
2502.13073 | Network-Realized Model Predictive Control Part II: Distributed
Constraint Management | eess.SY cs.SY | A two-layer control architecture is proposed, which promotes scalable
implementations for model predictive controllers. The top layer acts as both
reference governor for the bottom layer, and as a feedback controller for the
regulated network. By employing set-based methods, global theoretical
guarantees are obtained... |
2502.13076 | KAPPA: A Generic Patent Analysis Framework with Keyphrase-Based
Portraits | cs.CL | Patent analysis highly relies on concise and interpretable document
representations, referred to as patent portraits. Keyphrases, both present and
absent, are ideal candidates for patent portraits due to their brevity,
representativeness, and clarity. In this paper, we introduce KAPPA, an
integrated framework designe... |
2502.13077 | Pricing is All You Need to Improve Traffic Routing | eess.SY cs.SY | We investigate the design of pricing policies that enhance driver adherence
to route guidance, ensuring effective routing control. The major novelty lies
in that we adopt a Markov chain to model drivers' compliance rates conditioned
on both traffic states and tolls. By formulating the managed traffic network as
a non... |
2502.13078 | L4P: Low-Level 4D Vision Perception Unified | cs.CV | The spatio-temporal relationship between the pixels of a video carries
critical information for low-level 4D perception. A single model that reasons
about it should be able to solve several such tasks well. Yet, most
state-of-the-art methods rely on architectures specialized for the task at
hand. We present L4P (pron... |
2502.13080 | BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression
Classification | cs.LG cs.AI | Gene expression classification is a pivotal yet challenging task in
bioinformatics, primarily due to the high dimensionality of genomic data and
the risk of overfitting. To bridge this gap, we propose BOLIMES, a novel
feature selection algorithm designed to enhance gene expression classification
by systematically ref... |
2502.13081 | Personalized Image Generation with Deep Generative Models: A Decade
Survey | cs.CV | Recent advancements in generative models have significantly facilitated the
development of personalized content creation. Given a small set of images with
user-specific concept, personalized image generation allows to create images
that incorporate the specified concept and adhere to provided text
descriptions. Due t... |
2502.13082 | Automated Linear Parameter-Varying Modeling of Nonlinear Systems: A
Global Embedding Approach | eess.SY cs.SY | In this paper, an automated Linear Parameter-Varying (LPV) model conversion
approach is proposed for nonlinear dynamical systems. The proposed method
achieves global embedding of the original nonlinear behavior of the system by
leveraging the second fundamental theorem of calculus to factorize matrix
function express... |
2502.13085 | A Neural Difference-of-Entropies Estimator for Mutual Information | stat.ML cs.IT cs.LG math.IT | Estimating Mutual Information (MI), a key measure of dependence of random
quantities without specific modelling assumptions, is a challenging problem in
high dimensions. We propose a novel mutual information estimator based on
parametrizing conditional densities using normalizing flows, a deep generative
model that h... |
2502.13090 | tn4ml: Tensor Network Training and Customization for Machine Learning | cs.LG cs.MS quant-ph | Tensor Networks have emerged as a prominent alternative to neural networks
for addressing Machine Learning challenges in foundational sciences, paving the
way for their applications to real-life problems. This paper introduces tn4ml,
a novel library designed to seamlessly integrate Tensor Networks into
optimization p... |
2502.13092 | Text2World: Benchmarking Large Language Models for Symbolic World Model
Generation | cs.CL cs.AI | Recently, there has been growing interest in leveraging large language models
(LLMs) to generate symbolic world models from textual descriptions. Although
LLMs have been extensively explored in the context of world modeling, prior
studies encountered several challenges, including evaluation randomness,
dependence on ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.