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2502.06485
|
WyckoffDiff -- A Generative Diffusion Model for Crystal Symmetry
|
cond-mat.mtrl-sci cs.AI cs.LG
|
Crystalline materials often exhibit a high level of symmetry. However, most
generative models do not account for symmetry, but rather model each atom
without any constraints on its position or element. We propose a generative
model, Wyckoff Diffusion (WyckoffDiff), which generates symmetry-based
descriptions of crystals. This is enabled by considering a crystal structure
representation that encodes all symmetry, and we design a novel neural network
architecture which enables using this representation inside a discrete
generative model framework. In addition to respecting symmetry by construction,
the discrete nature of our model enables fast generation. We additionally
present a new metric, Fr\'echet Wrenformer Distance, which captures the
symmetry aspects of the materials generated, and we benchmark WyckoffDiff
against recently proposed generative models for crystal generation.
|
2502.06486
|
Biomechanical Reconstruction with Confidence Intervals from Multiview
Markerless Motion Capture
|
cs.CV
|
Advances in multiview markerless motion capture (MMMC) promise high-quality
movement analysis for clinical practice and research. While prior validation
studies show MMMC performs well on average, they do not provide what is needed
in clinical practice or for large-scale utilization of MMMC -- confidence
intervals over specific kinematic estimates from a specific individual analyzed
using a possibly unique camera configuration. We extend our previous work using
an implicit representation of trajectories optimized end-to-end through a
differentiable biomechanical model to learn the posterior probability
distribution over pose given all the detected keypoints. This posterior
probability is learned through a variational approximation and estimates
confidence intervals for individual joints at each moment in a trial, showing
confidence intervals generally within 10-15 mm of spatial error for virtual
marker locations, consistent with our prior validation studies. Confidence
intervals over joint angles are typically only a few degrees and widen for more
distal joints. The posterior also models the correlation structure over joint
angles, such as correlations between hip and pelvis angles. The confidence
intervals estimated through this method allow us to identify times and trials
where kinematic uncertainty is high.
|
2502.06487
|
Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection
|
cs.CL
|
Recent advances on instruction fine-tuning have led to the development of
various prompting techniques for large language models, such as explicit
reasoning steps. However, the success of techniques depends on various
parameters, such as the task, language model, and context provided. Finding an
effective prompt is, therefore, often a trial-and-error process. Most existing
approaches to automatic prompting aim to optimize individual techniques instead
of compositions of techniques and their dependence on the input. To fill this
gap, we propose an adaptive prompting approach that predicts the optimal prompt
composition ad-hoc for a given input. We apply our approach to social bias
detection, a highly context-dependent task that requires semantic
understanding. We evaluate it with three large language models on three
datasets, comparing compositions to individual techniques and other baselines.
The results underline the importance of finding an effective prompt
composition. Our approach robustly ensures high detection performance, and is
best in several settings. Moreover, first experiments on other tasks support
its generalizability.
|
2502.06490
|
Recent Advances in Discrete Speech Tokens: A Review
|
eess.AS cs.AI cs.MM cs.SD eess.SP
|
The rapid advancement of speech generation technologies in the era of large
language models (LLMs) has established discrete speech tokens as a foundational
paradigm for speech representation. These tokens, characterized by their
discrete, compact, and concise nature, are not only advantageous for efficient
transmission and storage, but also inherently compatible with the language
modeling framework, enabling seamless integration of speech into text-dominated
LLM architectures. Current research categorizes discrete speech tokens into two
principal classes: acoustic tokens and semantic tokens, each of which has
evolved into a rich research domain characterized by unique design philosophies
and methodological approaches. This survey systematically synthesizes the
existing taxonomy and recent innovations in discrete speech tokenization,
conducts a critical examination of the strengths and limitations of each
paradigm, and presents systematic experimental comparisons across token types.
Furthermore, we identify persistent challenges in the field and propose
potential research directions, aiming to offer actionable insights to inspire
future advancements in the development and application of discrete speech
tokens.
|
2502.06491
|
Model-Based Offline Reinforcement Learning with Reliability-Guaranteed
Sequence Modeling
|
cs.LG cs.AI
|
Model-based offline reinforcement learning (MORL) aims to learn a policy by
exploiting a dynamics model derived from an existing dataset. Applying
conservative quantification to the dynamics model, most existing works on MORL
generate trajectories that approximate the real data distribution to facilitate
policy learning by using current information (e.g., the state and action at
time step $t$). However, these works neglect the impact of historical
information on environmental dynamics, leading to the generation of unreliable
trajectories that may not align with the real data distribution. In this paper,
we propose a new MORL algorithm \textbf{R}eliability-guaranteed
\textbf{T}ransformer (RT), which can eliminate unreliable trajectories by
calculating the cumulative reliability of the generated trajectory (i.e., using
a weighted variational distance away from the real data). Moreover, by sampling
candidate actions with high rewards, RT can efficiently generate high-return
trajectories from the existing offline data. We theoretically prove the
performance guarantees of RT in policy learning, and empirically demonstrate
its effectiveness against state-of-the-art model-based methods on several
benchmark tasks.
|
2502.06494
|
GuideLLM: Exploring LLM-Guided Conversation with Applications in
Autobiography Interviewing
|
cs.CL cs.AI
|
Although Large Language Models (LLMs) succeed in human-guided conversations
such as instruction following and question answering, the potential of
LLM-guided conversations-where LLMs direct the discourse and steer the
conversation's objectives-remains under-explored. In this study, we first
characterize LLM-guided conversation into three fundamental components: (i)
Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and
propose GuideLLM as an installation. We then implement an interviewing
environment for the evaluation of LLM-guided conversation. Specifically,
various topics are involved in this environment for comprehensive interviewing
evaluation, resulting in around 1.4k turns of utterances, 184k tokens, and over
200 events mentioned during the interviewing for each chatbot evaluation. We
compare GuideLLM with 6 state-of-the-art LLMs such as GPT-4o and
Llama-3-70b-Instruct, from the perspective of interviewing quality, and
autobiography generation quality. For automatic evaluation, we derive user
proxies from multiple autobiographies and employ LLM-as-a-judge to score LLM
behaviors. We further conduct a human-involved experiment by employing 45 human
participants to chat with GuideLLM and baselines. We then collect human
feedback, preferences, and ratings regarding the qualities of conversation and
autobiography. Experimental results indicate that GuideLLM significantly
outperforms baseline LLMs in automatic evaluation and achieves consistent
leading performances in human ratings.
|
2502.06498
|
Decision Boundary Optimization-Informed Domain Adaptation
|
cs.CV
|
Maximum Mean Discrepancy (MMD) is widely used in a number of domain
adaptation (DA) methods and shows its effectiveness in aligning data
distributions across domains. However, in previous DA research, MMD-based DA
methods focus mostly on distribution alignment, and ignore to optimize the
decision boundary for classification-aware DA, thereby falling short in
reducing the DA upper error bound. In this paper, we propose a strengthened MMD
measurement, namely, Decision Boundary optimization-informed MMD (DB-MMD),
which enables MMD to carefully take into account the decision boundaries,
thereby simultaneously optimizing the distribution alignment and cross-domain
classifier within a hybrid framework, and leading to a theoretical bound guided
DA. We further seamlessly embed the proposed DB-MMD measurement into several
popular DA methods, e.g., MEDA, DGA-DA, to demonstrate its effectiveness w.r.t
different experimental settings. We carry out comprehensive experiments using 8
standard DA datasets. The experimental results show that the DB-MMD enforced DA
methods improve their baseline models using plain vanilla MMD, with a margin
that can be as high as 9.5.
|
2502.06501
|
Learning Clustering-based Prototypes for Compositional Zero-shot
Learning
|
cs.CV
|
Learning primitive (i.e., attribute and object) concepts from seen
compositions is the primary challenge of Compositional Zero-Shot Learning
(CZSL). Existing CZSL solutions typically rely on oversimplified data
assumptions, e.g., modeling each primitive with a single centroid primitive
representation, ignoring the natural diversities of the attribute (resp.
object) when coupled with different objects (resp. attribute). In this work, we
develop ClusPro, a robust clustering-based prototype mining framework for CZSL
that defines the conceptual boundaries of primitives through a set of
diversified prototypes. Specifically, ClusPro conducts within-primitive
clustering on the embedding space for automatically discovering and dynamically
updating prototypes. These representative prototypes are subsequently used to
repaint a well-structured and independent primitive embedding space, ensuring
intra-primitive separation and inter-primitive decorrelation through
prototype-based contrastive learning and decorrelation learning. Moreover,
ClusPro efficiently performs prototype clustering in a non-parametric fashion
without the introduction of additional learnable parameters or computational
budget during testing. Experiments on three benchmarks demonstrate ClusPro
outperforms various top-leading CZSL solutions under both closed-world and
open-world settings.
|
2502.06516
|
Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation
|
cs.LG cs.AI cs.CV stat.ML
|
Minority samples are underrepresented instances located in low-density
regions of a data manifold, and are valuable in many generative AI
applications, such as data augmentation, creative content generation, etc.
Unfortunately, existing diffusion-based minority generators often rely on
computationally expensive guidance dedicated for minority generation. To
address this, here we present a simple yet powerful guidance-free approach
called Boost-and-Skip for generating minority samples using diffusion models.
The key advantage of our framework requires only two minimal changes to
standard generative processes: (i) variance-boosted initialization and (ii)
timestep skipping. We highlight that these seemingly-trivial modifications are
supported by solid theoretical and empirical evidence, thereby effectively
promoting emergence of underrepresented minority features. Our comprehensive
experiments demonstrate that Boost-and-Skip greatly enhances the capability of
generating minority samples, even rivaling guidance-based state-of-the-art
approaches while requiring significantly fewer computations.
|
2502.06519
|
SIREN: Semantic, Initialization-Free Registration of Multi-Robot
Gaussian Splatting Maps
|
cs.RO cs.CV
|
We present SIREN for registration of multi-robot Gaussian Splatting (GSplat)
maps, with zero access to camera poses, images, and inter-map transforms for
initialization or fusion of local submaps. To realize these capabilities, SIREN
harnesses the versatility and robustness of semantics in three critical ways to
derive a rigorous registration pipeline for multi-robot GSplat maps. First,
SIREN utilizes semantics to identify feature-rich regions of the local maps
where the registration problem is better posed, eliminating the need for any
initialization which is generally required in prior work. Second, SIREN
identifies candidate correspondences between Gaussians in the local maps using
robust semantic features, constituting the foundation for robust geometric
optimization, coarsely aligning 3D Gaussian primitives extracted from the local
maps. Third, this key step enables subsequent photometric refinement of the
transformation between the submaps, where SIREN leverages novel-view synthesis
in GSplat maps along with a semantics-based image filter to compute a
high-accuracy non-rigid transformation for the generation of a high-fidelity
fused map. We demonstrate the superior performance of SIREN compared to
competing baselines across a range of real-world datasets, and in particular,
across the most widely-used robot hardware platforms, including a manipulator,
drone, and quadruped. In our experiments, SIREN achieves about 90x smaller
rotation errors, 300x smaller translation errors, and 44x smaller scale errors
in the most challenging scenes, where competing methods struggle. We will
release the code and provide a link to the project page after the review
process.
|
2502.06523
|
Tighter Value-Function Approximations for POMDPs
|
cs.AI
|
Solving partially observable Markov decision processes (POMDPs) typically
requires reasoning about the values of exponentially many state beliefs.
Towards practical performance, state-of-the-art solvers use value bounds to
guide this reasoning. However, sound upper value bounds are often
computationally expensive to compute, and there is a tradeoff between the
tightness of such bounds and their computational cost. This paper introduces
new and provably tighter upper value bounds than the commonly used fast
informed bound. Our empirical evaluation shows that, despite their additional
computational overhead, the new upper bounds accelerate state-of-the-art POMDP
solvers on a wide range of benchmarks.
|
2502.06525
|
Properties of Wasserstein Gradient Flows for the Sliced-Wasserstein
Distance
|
stat.ML cs.LG
|
In this paper, we investigate the properties of the Sliced Wasserstein
Distance (SW) when employed as an objective functional. The SW metric has
gained significant interest in the optimal transport and machine learning
literature, due to its ability to capture intricate geometric properties of
probability distributions while remaining computationally tractable, making it
a valuable tool for various applications, including generative modeling and
domain adaptation. Our study aims to provide a rigorous analysis of the
critical points arising from the optimization of the SW objective. By computing
explicit perturbations, we establish that stable critical points of SW cannot
concentrate on segments. This stability analysis is crucial for understanding
the behaviour of optimization algorithms for models trained using the SW
objective. Furthermore, we investigate the properties of the SW objective,
shedding light on the existence and convergence behavior of critical points. We
illustrate our theoretical results through numerical experiments.
|
2502.06526
|
Convex Split Lemma without Inequalities
|
quant-ph cs.IT math-ph math.IT math.MP
|
We introduce a refinement to the convex split lemma by replacing the max
mutual information with the collision mutual information, transforming the
inequality into an equality. This refinement yields tighter achievability
bounds for quantum source coding tasks, including state merging and state
splitting. Furthermore, we derive a universal upper bound on the smoothed max
mutual information, where "universal" signifies that the bound depends
exclusively on R\'enyi entropies and is independent of the system's dimensions.
This result has significant implications for quantum information processing,
particularly in applications such as the reverse quantum Shannon theorem.
|
2502.06527
|
CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for
Zero-Shot Customized Video Diffusion Transformers
|
cs.CV
|
Customized generation has achieved significant progress in image synthesis,
yet personalized video generation remains challenging due to temporal
inconsistencies and quality degradation. In this paper, we introduce
CustomVideoX, an innovative framework leveraging the video diffusion
transformer for personalized video generation from a reference image.
CustomVideoX capitalizes on pre-trained video networks by exclusively training
the LoRA parameters to extract reference features, ensuring both efficiency and
adaptability. To facilitate seamless interaction between the reference image
and video content, we propose 3D Reference Attention, which enables direct and
simultaneous engagement of reference image features with all video frames
across spatial and temporal dimensions. To mitigate the excessive influence of
reference image features and textual guidance on generated video content during
inference, we implement the Time-Aware Reference Attention Bias (TAB) strategy,
dynamically modulating reference bias over different time steps. Additionally,
we introduce the Entity Region-Aware Enhancement (ERAE) module, aligning highly
activated regions of key entity tokens with reference feature injection by
adjusting attention bias. To thoroughly evaluate personalized video generation,
we establish a new benchmark, VideoBench, comprising over 50 objects and 100
prompts for extensive assessment. Experimental results show that CustomVideoX
significantly outperforms existing methods in terms of video consistency and
quality.
|
2502.06533
|
Ignore the KL Penalty! Boosting Exploration on Critical Tokens to
Enhance RL Fine-Tuning
|
cs.CL cs.LG
|
The ability to achieve long-term goals is a key challenge in the current
development of large language models (LLMs). To address this, pre-trained LLMs
can be fine-tuned with reinforcement learning (RL) to explore solutions that
optimize a given goal. However, exploration with LLMs is difficult, as a
balance has to be struck between discovering new solutions and staying close
enough to the pre-trained model, so as not to degrade basic capabilities. This
is typically controlled with a Kullback-Leibler (KL) penalty. In this paper, we
investigate the exploration dynamics of a small language model on a simple
arithmetic task. We show how varying degrees of pre-training influence
exploration and demonstrate the importance of "critical tokens" which have a
dramatic impact on the final outcome. Consequently, we introduce a simple
modification to the KL penalty that favors exploration on critical tokens,
increasing the efficiency of the RL fine-tuning stage.
|
2502.06536
|
Sample-efficient Learning of Concepts with Theoretical Guarantees: from
Data to Concepts without Interventions
|
stat.ML cs.LG
|
Machine learning is a vital part of many real-world systems, but several
concerns remain about the lack of interpretability, explainability and
robustness of black-box AI systems. Concept-based models (CBM) address some of
these challenges by learning interpretable concepts from high-dimensional data,
e.g. images, which are used to predict labels. An important issue in CBMs is
concept leakage, i.e., spurious information in the learned concepts, which
effectively leads to learning "wrong" concepts. Current mitigating strategies
are heuristic, have strong assumptions, e.g., they assume that the concepts are
statistically independent of each other, or require substantial human
interaction in terms of both interventions and labels provided by annotators.
In this paper, we describe a framework that provides theoretical guarantees on
the correctness of the learned concepts and on the number of required labels,
without requiring any interventions. Our framework leverages causal
representation learning (CRL) to learn high-level causal variables from
low-level data, and learns to align these variables with interpretable
concepts. We propose a linear and a non-parametric estimator for this mapping,
providing a finite-sample high probability result in the linear case and an
asymptotic consistency result for the non-parametric estimator. We implement
our framework with state-of-the-art CRL methods, and show its efficacy in
learning the correct concepts in synthetic and image benchmarks.
|
2502.06543
|
Unsupervised Learning for Feature Extraction and Temporal Alignment of
3D+t Point Clouds of Zebrafish Embryos
|
cs.CV
|
Zebrafish are widely used in biomedical research and developmental stages of
their embryos often need to be synchronized for further analysis. We present an
unsupervised approach to extract descriptive features from 3D+t point clouds of
zebrafish embryos and subsequently use those features to temporally align
corresponding developmental stages. An autoencoder architecture is proposed to
learn a descriptive representation of the point clouds and we designed a deep
regression network for their temporal alignment. We achieve a high alignment
accuracy with an average mismatch of only 3.83 minutes over an experimental
duration of 5.3 hours. As a fully-unsupervised approach, there is no manual
labeling effort required and unlike manual analyses the method easily scales.
Besides, the alignment without human annotation of the data also avoids any
influence caused by subjective bias.
|
2502.06544
|
Sequence Transferability and Task Order Selection in Continual Learning
|
cs.LG cs.CV
|
In continual learning, understanding the properties of task sequences and
their relationships to model performance is important for developing advanced
algorithms with better accuracy. However, efforts in this direction remain
underdeveloped despite encouraging progress in methodology development. In this
work, we investigate the impacts of sequence transferability on continual
learning and propose two novel measures that capture the total transferability
of a task sequence, either in the forward or backward direction. Based on the
empirical properties of these measures, we then develop a new method for the
task order selection problem in continual learning. Our method can be shown to
offer a better performance than the conventional strategy of random task
selection.
|
2502.06545
|
Dimension-free Regret for Learning Asymmetric Linear Dynamical Systems
|
cs.LG stat.ML
|
Previously, methods for learning marginally stable linear dynamical systems
either required the transition matrix to be symmetric or incurred regret bounds
that scale polynomially with the system's hidden dimension. In this work, we
introduce a novel method that overcomes this trade-off, achieving
dimension-free regret despite the presence of asymmetric matrices and marginal
stability. Our method combines spectral filtering with linear predictors and
employs Chebyshev polynomials in the complex plane to construct a novel
spectral filtering basis. This construction guarantees sublinear regret in an
online learning framework, without relying on any statistical or generative
assumptions. Specifically, we prove that as long as the transition matrix has
eigenvalues with complex component bounded by $1/\mathrm{poly} \log T$, then
our method achieves regret $\tilde{O}(T^{9/10})$ when compared to the best
linear dynamical predictor in hindsight.
|
2502.06547
|
Data Augmentation and Regularization for Learning Group Equivariance
|
stat.ML cs.LG math.OC
|
In many machine learning tasks, known symmetries can be used as an inductive
bias to improve model performance. In this paper, we consider learning group
equivariance through training with data augmentation. We summarize results from
a previous paper of our own, and extend the results to show that equivariance
of the trained model can be achieved through training on augmented data in
tandem with regularization.
|
2502.06551
|
Efficient Scientific Full Text Classification: The Case of EICAT Impact
Assessments
|
cs.CL
|
This study explores strategies for efficiently classifying scientific full
texts using both small, BERT-based models and local large language models like
Llama-3.1 8B. We focus on developing methods for selecting subsets of input
sentences to reduce input size while simultaneously enhancing classification
performance. To this end, we compile a novel dataset consisting of full-text
scientific papers from the field of invasion biology, specifically addressing
the impacts of invasive species. These papers are aligned with publicly
available impact assessments created by researchers for the International Union
for Conservation of Nature (IUCN). Through extensive experimentation, we
demonstrate that various sources like human evidence annotations, LLM-generated
annotations or explainability scores can be used to train sentence selection
models that improve the performance of both encoder- and decoder-based language
models while optimizing efficiency through the reduction in input length,
leading to improved results even if compared to models like ModernBERT that are
able to handle the complete text as input. Additionally, we find that repeated
sampling of shorter inputs proves to be a very effective strategy that, at a
slightly increased cost, can further improve classification performance.
|
2502.06552
|
Diffusion Models for Computational Neuroimaging: A Survey
|
cs.CV
|
Computational neuroimaging involves analyzing brain images or signals to
provide mechanistic insights and predictive tools for human cognition and
behavior. While diffusion models have shown stability and high-quality
generation in natural images, there is increasing interest in adapting them to
analyze brain data for various neurological tasks such as data enhancement,
disease diagnosis and brain decoding. This survey provides an overview of
recent efforts to integrate diffusion models into computational neuroimaging.
We begin by introducing the common neuroimaging data modalities, follow with
the diffusion formulations and conditioning mechanisms. Then we discuss how the
variations of the denoising starting point, condition input and generation
target of diffusion models are developed and enhance specific neuroimaging
tasks. For a comprehensive overview of the ongoing research, we provide a
publicly available repository at https://github.com/JoeZhao527/dm4neuro.
|
2502.06555
|
Is API Access to LLMs Useful for Generating Private Synthetic Tabular
Data?
|
cs.LG cs.CR
|
Differentially private (DP) synthetic data is a versatile tool for enabling
the analysis of private data. Recent advancements in large language models
(LLMs) have inspired a number of algorithm techniques for improving DP
synthetic data generation. One family of approaches uses DP finetuning on the
foundation model weights; however, the model weights for state-of-the-art
models may not be public. In this work we propose two DP synthetic tabular data
algorithms that only require API access to the foundation model. We adapt the
Private Evolution algorithm (Lin et al., 2023; Xie et al., 2024) -- which was
designed for image and text data -- to the tabular data domain. In our
extension of Private Evolution, we define a query workload-based distance
measure, which may be of independent interest. We propose a family of
algorithms that use one-shot API access to LLMs, rather than adaptive queries
to the LLM. Our findings reveal that API-access to powerful LLMs does not
always improve the quality of DP synthetic data compared to established
baselines that operate without such access. We provide insights into the
underlying reasons and propose improvements to LLMs that could make them more
effective for this application.
|
2502.06556
|
ProjectTest: A Project-level LLM Unit Test Generation Benchmark and
Impact of Error Fixing Mechanisms
|
cs.SE cs.CL
|
Unit test generation has become a promising and important use case of LLMs.
However, existing evaluation benchmarks for assessing LLM unit test generation
capabilities focus on function- or class-level code rather than more practical
and challenging project-level codebases. To address such limitation, we propose
ProjectTest, a project-level benchmark for unit test generation covering
Python, Java, and JavaScript. ProjectTest features 20 moderate-sized and
high-quality projects per language. We evaluate nine frontier LLMs on
ProjectTest and the results show that all frontier LLMs tested exhibit moderate
performance on ProjectTest on Python and Java, highlighting the difficulty of
ProjectTest. We also conduct a thorough error analysis, which shows that even
frontier LLMs, such as Claude-3.5-Sonnet, have significant basic yet critical
errors, including compilation and cascade errors. Motivated by this
observation, we further evaluate all frontier LLMs under manual error-fixing
and self-error-fixing scenarios to assess their potential when equipped with
error-fixing mechanisms. Our code and dataset is available at
\href{https://github.com/YiboWANG214/ProjectTest}{ProjectTest}.
|
2502.06557
|
LiveForesighter: Generating Future Information for Live-Streaming
Recommendations at Kuaishou
|
cs.IR
|
Live-streaming, as a new-generation media to connect users and authors, has
attracted a lot of attention and experienced rapid growth in recent years.
Compared with the content-static short-video recommendation, the live-streaming
recommendation faces more challenges in giving our users a satisfactory
experience: (1) Live-streaming content is dynamically ever-changing along time.
(2) valuable behaviors (e.g., send digital-gift, buy products) always require
users to watch for a long-time (>10 min). Combining the two attributes, here
raising a challenging question for live-streaming recommendation: How to
discover the live-streamings that the content user is interested in at the
current moment, and further a period in the future?
|
2502.06559
|
Can We Trust AI Benchmarks? An Interdisciplinary Review of Current
Issues in AI Evaluation
|
cs.AI
|
Quantitative Artificial Intelligence (AI) Benchmarks have emerged as
fundamental tools for evaluating the performance, capability, and safety of AI
models and systems. Currently, they shape the direction of AI development and
are playing an increasingly prominent role in regulatory frameworks. As their
influence grows, however, so too does concerns about how and with what effects
they evaluate highly sensitive topics such as capabilities, including
high-impact capabilities, safety and systemic risks. This paper presents an
interdisciplinary meta-review of about 100 studies that discuss shortcomings in
quantitative benchmarking practices, published in the last 10 years. It brings
together many fine-grained issues in the design and application of benchmarks
(such as biases in dataset creation, inadequate documentation, data
contamination, and failures to distinguish signal from noise) with broader
sociotechnical issues (such as an over-focus on evaluating text-based AI models
according to one-time testing logic that fails to account for how AI models are
increasingly multimodal and interact with humans and other technical systems).
Our review also highlights a series of systemic flaws in current benchmarking
practices, such as misaligned incentives, construct validity issues, unknown
unknowns, and problems with the gaming of benchmark results. Furthermore, it
underscores how benchmark practices are fundamentally shaped by cultural,
commercial and competitive dynamics that often prioritise state-of-the-art
performance at the expense of broader societal concerns. By providing an
overview of risks associated with existing benchmarking procedures, we
problematise disproportionate trust placed in benchmarks and contribute to
ongoing efforts to improve the accountability and relevance of quantitative AI
benchmarks within the complexities of real-world scenarios.
|
2502.06560
|
Position: It's Time to Act on the Risk of Efficient Personalized Text
Generation
|
cs.CL cs.CY
|
The recent surge in high-quality open-sourced Generative AI text models
(colloquially: LLMs), as well as efficient finetuning techniques, has opened
the possibility of creating high-quality personalized models, i.e., models
generating text attuned to a specific individual's needs and capable of
credibly imitating their writing style by leveraging that person's own data to
refine an open-source model. The technology to create such models is accessible
to private individuals, and training and running such models can be done
cheaply on consumer-grade hardware. These advancements are a huge gain for
usability and privacy. This position paper argues, however, that these
advancements also introduce new safety risks by making it practically feasible
for malicious actors to impersonate specific individuals at scale, for instance
for the purpose of phishing emails, based on small amounts of publicly
available text. We further argue that these risks are complementary to - and
distinct from - the much-discussed risks of other impersonation attacks such as
image, voice, or video deepfakes, and are not adequately addressed by the
larger research community, or the current generation of open - and
closed-source models.
|
2502.06563
|
Large Language Models Meet Symbolic Provers for Logical Reasoning
Evaluation
|
cs.CL
|
First-order logic (FOL) reasoning, which involves sequential deduction, is
pivotal for intelligent systems and serves as a valuable task for evaluating
reasoning capabilities, particularly in chain-of-thought (CoT) contexts.
Existing benchmarks often rely on extensive human annotation or handcrafted
templates, making it difficult to achieve the necessary complexity,
scalability, and diversity for robust evaluation. To address these limitations,
we propose a novel framework called ProverGen that synergizes the generative
strengths of Large Language Models (LLMs) with the rigor and precision of
symbolic provers, enabling the creation of a scalable, diverse, and
high-quality FOL reasoning dataset, ProverQA. ProverQA is also distinguished by
its inclusion of accessible and logically coherent intermediate reasoning steps
for each problem. Our evaluation shows that state-of-the-art LLMs struggle to
solve ProverQA problems, even with CoT prompting, highlighting the dataset's
challenging nature. We also finetune Llama3.1-8B-Instruct on a separate
training set generated by our framework. The finetuned model demonstrates
consistent improvements on both in-distribution and out-of-distribution test
sets, suggesting the value of our proposed data generation framework. Code
available at: https://github.com/opendatalab/ProverGen
|
2502.06564
|
Robust Scatter Matrix Estimation for Elliptical Distributions in
Polynomial Time
|
cs.DS cs.LG math.ST stat.ML stat.TH
|
We study the problem of computationally efficient robust estimation of
scatter matrices of elliptical distributions under the strong contamination
model. We design polynomial time algorithms that achieve dimension-independent
error in Frobenius norm.
Our first result is a sequence of efficient algorithms that approaches nearly
optimal error. Specifically, under a mild assumption on the eigenvalues of the
scatter matrix $\Sigma$, for every $t \in \mathbb{N}$, we design an estimator
that, given $n = d^{O(t)}$ samples, in time $n^{O(t)}$ finds $\hat{\Sigma}$
such that $ \Vert{\Sigma^{-1/2}\, ({\hat{\Sigma} - \Sigma})\,
\Sigma^{-1/2}}\Vert_{\text{F}} \le O(t \cdot \varepsilon^{1-\frac{1}{t}})$,
where $\varepsilon$ is the fraction of corruption. We do not require any
assumptions on the moments of the distribution, while all previously known
computationally efficient algorithms for robust covariance/scatter estimation
with dimension-independent error rely on strong assumptions on the moments,
such as sub-Gaussianity or (certifiable) hypercontractivity.
Furthermore, under a stronger assumption on the eigenvalues of $\Sigma$
(that, in particular, is satisfied by all matrices with constant condition
number),
we provide a fast (sub-quadratic in the input size) algorithm that, given
nearly optimal number of samples $n = \tilde{O}(d^2/\varepsilon)$, in time
$\tilde{O}({nd^2 poly(1/\varepsilon)})$ finds $\hat{\Sigma}$ such that
$\Vert\hat{\Sigma} - \Sigma\Vert_{\text{F}} \le O(\Vert{\Sigma}\Vert \cdot
\sqrt{\varepsilon})$.
Our approach is based on robust covariance estimation of the spatial sign
(the projection onto the sphere of radius $\sqrt{d}$) of elliptical
distributions.
|
2502.06567
|
Membership Inference Risks in Quantized Models: A Theoretical and
Empirical Study
|
stat.ML cs.LG
|
Quantizing machine learning models has demonstrated its effectiveness in
lowering memory and inference costs while maintaining performance levels
comparable to the original models. In this work, we investigate the impact of
quantization procedures on the privacy of data-driven models, specifically
focusing on their vulnerability to membership inference attacks. We derive an
asymptotic theoretical analysis of Membership Inference Security (MIS),
characterizing the privacy implications of quantized algorithm weights against
the most powerful (and possibly unknown) attacks. Building on these theoretical
insights, we propose a novel methodology to empirically assess and rank the
privacy levels of various quantization procedures. Using synthetic datasets, we
demonstrate the effectiveness of our approach in assessing the MIS of different
quantizers. Furthermore, we explore the trade-off between privacy and
performance using real-world data and models in the context of molecular
modeling.
|
2502.06572
|
LawGPT: Knowledge-Guided Data Generation and Its Application to Legal
LLM
|
cs.CL cs.AI
|
Large language models (LLMs), both proprietary and open-source, have
demonstrated remarkable capabilities across various natural language processing
tasks. However, they face significant limitations in legal reasoning tasks.
Proprietary models introduce data privacy risks and high inference costs, while
open-source models underperform due to insufficient legal domain training data.
To address these limitations, we study data generation for legal reasoning to
improve the legal reasoning performance of open-source LLMs with the help of
proprietary LLMs. This is challenging due to the lack of legal knowledge in
proprietary LLMs and the difficulty in verifying the generated data. We propose
KgDG, a knowledge-guided data generation framework for legal reasoning. Our
framework enables leveraging legal knowledge to enhance generation diversity
and introduces a refinement and verification process to ensure the quality of
generated data. Moreover, we expand the generated dataset to further enhance
the LLM reasoning capabilities. Using KgDG, we create a synthetic legal
reasoning dataset containing 50K high-quality examples. Our trained model
LawGPT outperforms existing legal-specific LLMs and achieves performance
comparable to proprietary LLMs, demonstrating the effectiveness of KgDG and
LawGPT. Our code and resources is publicly available at
https://github.com/LAMDASZ-ML/Knowledge-Guide-Data-Generation .
|
2502.06574
|
On the Impact of the Utility in Semivalue-based Data Valuation
|
cs.AI cs.GT cs.LG
|
Semivalue-based data valuation in machine learning (ML) quantifies the
contribution of individual data points to a downstream ML task by leveraging
principles from cooperative game theory and the notion of utility. While this
framework has been used in practice for assessing data quality, our experiments
reveal inconsistent valuation outcomes across different utilities, albeit all
related to ML performance. Beyond raising concerns about the reliability of
data valuation, this inconsistency is challenging to interpret, as it stems
from the complex interaction of the utility with data points and semivalue
weights, which has barely been studied in prior work. In this paper, we take a
first step toward clarifying the utility impact on semivalue-based data
valuation. Specifically, we provide geometric interpretations of this impact
for a broad family of classification utilities, which includes the accuracy and
the arithmetic mean. We introduce the notion of spatial signatures: given a
semivalue, data points can be embedded into a two-dimensional space, and
utility functions map to the dual of this space. This geometric perspective
separates the influence of the dataset and semivalue from that of the utility,
providing a theoretical explanation for the experimentally observed sensitivity
of valuation outcomes to the utility choice.
|
2502.06575
|
Predictive Red Teaming: Breaking Policies Without Breaking Robots
|
cs.RO cs.AI cs.LG cs.SY eess.SY
|
Visuomotor policies trained via imitation learning are capable of performing
challenging manipulation tasks, but are often extremely brittle to lighting,
visual distractors, and object locations. These vulnerabilities can depend
unpredictably on the specifics of training, and are challenging to expose
without time-consuming and expensive hardware evaluations. We propose the
problem of predictive red teaming: discovering vulnerabilities of a policy with
respect to environmental factors, and predicting the corresponding performance
degradation without hardware evaluations in off-nominal scenarios. In order to
achieve this, we develop RoboART: an automated red teaming (ART) pipeline that
(1) modifies nominal observations using generative image editing to vary
different environmental factors, and (2) predicts performance under each
variation using a policy-specific anomaly detector executed on edited
observations. Experiments across 500+ hardware trials in twelve off-nominal
conditions for visuomotor diffusion policies demonstrate that RoboART predicts
performance degradation with high accuracy (less than 0.19 average difference
between predicted and real success rates). We also demonstrate how predictive
red teaming enables targeted data collection: fine-tuning with data collected
under conditions predicted to be adverse boosts baseline performance by 2-7x.
|
2502.06577
|
The Minimal Search Space for Conditional Causal Bandits
|
cs.LG cs.AI stat.ML
|
Causal knowledge can be used to support decision-making problems. This has
been recognized in the causal bandits literature, where a causal (multi-armed)
bandit is characterized by a causal graphical model and a target variable. The
arms are then interventions on the causal model, and rewards are samples of the
target variable. Causal bandits were originally studied with a focus on hard
interventions. We focus instead on cases where the arms are conditional
interventions, which more accurately model many real-world decision-making
problems by allowing the value of the intervened variable to be chosen based on
the observed values of other variables. This paper presents a graphical
characterization of the minimal set of nodes guaranteed to contain the optimal
conditional intervention, which maximizes the expected reward. We then propose
an efficient algorithm with a time complexity of $O(|V| + |E|)$ to identify
this minimal set of nodes. We prove that the graphical characterization and the
proposed algorithm are correct. Finally, we empirically demonstrate that our
algorithm significantly prunes the search space and substantially accelerates
convergence rates when integrated into standard multi-armed bandit algorithms.
|
2502.06580
|
Inventory Consensus Control in Supply Chain Networks using
Dissipativity-Based Control and Topology Co-Design
|
eess.SY cs.SY
|
Recent global and local phenomena have exposed vulnerabilities in critical
supply chain networks (SCNs), drawing significant attention from researchers
across various fields. Typically, SCNs are viewed as static entities regularly
optimized to maintain their optimal operation. However, the dynamic nature of
SCNs and their associated uncertainties have motivated researchers to treat
SCNs as dynamic networked systems requiring robust control techniques. In this
paper, we address the SCN inventory consensus problem, which aims to
synchronize multiple parallel supply chains, enhancing coordination and
robustness of the overall SCN. To achieve this, we take a novel approach
exploiting dissipativity theory. In particular, we propose a
dissipativity-based co-design strategy for distributed consensus controllers
and communication topology in SCNs. It requires only the dissipativity
information of the individual supply chains and involves solving a set of
convex optimization problems, thus contributing to scalability,
compositionality, and computational efficiency. Moreover, it optimizes the
robustness of the SCN to various associated uncertainties, mitigating both
bullwhip and ripple effects. We demonstrate our contributions using numerical
examples, mainly by comparing the consensus performance with respect to
standard steady-state control, feedback control, and consensus control
strategies.
|
2502.06581
|
A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems
|
cs.NI cs.CV cs.LG
|
The explosive growth of video data has driven the development of distributed
video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling
efficient video processing, real-time inference, and privacy-preserving
analysis. Among multiple advantages, CETC systems can distribute video
processing tasks and enable adaptive analytics across cloud, edge, and terminal
devices, leading to breakthroughs in video surveillance, autonomous driving,
and smart cities. In this survey, we first analyze fundamental architectural
components, including hierarchical, distributed, and hybrid frameworks,
alongside edge computing platforms and resource management mechanisms. Building
upon these foundations, edge-centric approaches emphasize on-device processing,
edge-assisted offloading, and edge intelligence, while cloud-centric methods
leverage powerful computational capabilities for complex video understanding
and model training. Our investigation also covers hybrid video analytics
incorporating adaptive task offloading and resource-aware scheduling techniques
that optimize performance across the entire system. Beyond conventional
approaches, recent advances in large language models and multimodal integration
reveal both opportunities and challenges in platform scalability, data
protection, and system reliability. Future directions also encompass
explainable systems, efficient processing mechanisms, and advanced video
analytics, offering valuable insights for researchers and practitioners in this
dynamic field.
|
2502.06583
|
Adaptive Perception for Unified Visual Multi-modal Object Tracking
|
cs.CV
|
Recently, many multi-modal trackers prioritize RGB as the dominant modality,
treating other modalities as auxiliary, and fine-tuning separately various
multi-modal tasks. This imbalance in modality dependence limits the ability of
methods to dynamically utilize complementary information from each modality in
complex scenarios, making it challenging to fully perceive the advantages of
multi-modal. As a result, a unified parameter model often underperforms in
various multi-modal tracking tasks. To address this issue, we propose APTrack,
a novel unified tracker designed for multi-modal adaptive perception. Unlike
previous methods, APTrack explores a unified representation through an equal
modeling strategy. This strategy allows the model to dynamically adapt to
various modalities and tasks without requiring additional fine-tuning between
different tasks. Moreover, our tracker integrates an adaptive modality
interaction (AMI) module that efficiently bridges cross-modality interactions
by generating learnable tokens. Experiments conducted on five diverse
multi-modal datasets (RGBT234, LasHeR, VisEvent, DepthTrack, and VOT-RGBD2022)
demonstrate that APTrack not only surpasses existing state-of-the-art unified
multi-modal trackers but also outperforms trackers designed for specific
multi-modal tasks.
|
2502.06584
|
Deep Reinforcement Learning based Triggering Function for Early
Classifiers of Time Series
|
cs.LG
|
Early Classification of Time Series (ECTS) has been recognized as an
important problem in many areas where decisions have to be taken as soon as
possible, before the full data availability, while time pressure increases.
Numerous ECTS approaches have been proposed, based on different triggering
functions, each taking into account various pieces of information related to
the incoming time series and/or the output of a classifier. Although their
performances have been empirically compared in the literature, no studies have
been carried out on the optimality of these triggering functions that involve
``man-tailored'' decision rules. Based on the same information, could there be
better triggering functions? This paper presents one way to investigate this
question by showing first how to translate ECTS problems into Reinforcement
Learning (RL) ones, where the very same information is used in the state space.
A thorough comparison of the performance obtained by ``handmade'' approaches
and their ``RL-based'' counterparts has been carried out. A second question
investigated in this paper is whether a different combination of information,
defining the state space in RL systems, can achieve even better performance.
Experiments show that the system we describe, called \textsc{Alert},
significantly outperforms its state-of-the-art competitors on a large number of
datasets.
|
2502.06585
|
Extract-QD Framework: A Generic Approach for Quality-Diversity in Noisy,
Stochastic or Uncertain Domains
|
cs.NE
|
Quality-Diversity (QD) has demonstrated potential in discovering collections
of diverse solutions to optimisation problems. Originally designed for
deterministic environments, QD has been extended to noisy, stochastic, or
uncertain domains through various Uncertain-QD (UQD) methods. However, the
large number of UQD methods, each with unique constraints, makes selecting the
most suitable one challenging. To remedy this situation, we present two
contributions: first, the Extract-QD Framework (EQD Framework), and second,
Extract-ME (EME), a new method derived from it. The EQD Framework unifies
existing approaches within a modular view, and facilitates developing novel
methods by interchanging modules. We use it to derive EME, a novel method that
consistently outperforms or matches the best existing methods on standard
benchmarks, while previous methods show varying performance. In a second
experiment, we show how our EQD Framework can be used to augment existing QD
algorithms and in particular the well-established
Policy-Gradient-Assisted-MAP-Elites method, and demonstrate improved
performance in uncertain domains at no additional evaluation cost. For any new
uncertain task, our contributions now provide EME as a reliable "first guess"
method, and the EQD Framework as a tool for developing task-specific
approaches. Together, these contributions aim to lower the cost of adopting UQD
insights in QD applications.
|
2502.06587
|
evclust: Python library for evidential clustering
|
cs.SE cs.CV cs.LG
|
A recent developing trend in clustering is the advancement of algorithms that
not only identify clusters within data, but also express and capture the
uncertainty of cluster membership. Evidential clustering addresses this by
using the Dempster-Shafer theory of belief functions, a framework designed to
manage and represent uncertainty. This approach results in a credal partition,
a structured set of mass functions that quantify the uncertain assignment of
each object to potential groups. The Python framework evclust, presented in
this paper, offers a suite of efficient evidence clustering algorithms as well
as tools for visualizing, evaluating and analyzing credal partitions.
|
2502.06589
|
Hephaestus: Improving Fundamental Agent Capabilities of Large Language
Models through Continual Pre-Training
|
cs.CL cs.AI cs.LG
|
Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous
agents typically rely on complex prompting or extensive fine-tuning, which
often fails to introduce new capabilities while preserving strong
generalizability. We introduce Hephaestus-Forge, the first large-scale
pre-training corpus designed to enhance the fundamental capabilities of LLM
agents in API function calling, intrinsic reasoning and planning, and adapting
to environmental feedback. Hephaestus-Forge comprises 103B agent-specific data
encompassing 76,537 APIs, including both tool documentation to introduce
knowledge of API functions and function calling trajectories to strengthen
intrinsic reasoning. To explore effective training protocols, we investigate
scaling laws to identify the optimal recipe in data mixing ratios. By continual
pre-training on Hephaestus-Forge, Hephaestus outperforms small- to medium-scale
open-source LLMs and rivals commercial LLMs on three agent benchmarks,
demonstrating the effectiveness of our pre-training corpus in enhancing
fundamental agentic capabilities and generalization of LLMs to new tasks or
environments.
|
2502.06591
|
Diffeomorphic Temporal Alignment Nets for Time-series Joint Alignment
and Averaging
|
cs.LG
|
In time-series analysis, nonlinear temporal misalignment remains a pivotal
challenge that forestalls even simple averaging. Since its introduction, the
Diffeomorphic Temporal Alignment Net (DTAN), which we first introduced (Weber
et al., 2019) and further developed in (Weber & Freifeld, 2023), has proven
itself as an effective solution for this problem (these conference papers are
earlier partial versions of the current manuscript). DTAN predicts and applies
diffeomorphic transformations in an input-dependent manner, thus facilitating
the joint alignment (JA) and averaging of time-series ensembles in an
unsupervised or a weakly-supervised manner. The inherent challenges of the
weakly/unsupervised setting, particularly the risk of trivial solutions through
excessive signal distortion, are mitigated using either one of two distinct
strategies: 1) a regularization term for warps; 2) using the Inverse
Consistency Averaging Error (ICAE). The latter is a novel, regularization-free
approach which also facilitates the JA of variable-length signals. We also
further extend our framework to incorporate multi-task learning (MT-DTAN),
enabling simultaneous time-series alignment and classification. Additionally,
we conduct a comprehensive evaluation of different backbone architectures,
demonstrating their efficacy in time-series alignment tasks. Finally, we
showcase the utility of our approach in enabling Principal Component Analysis
(PCA) for misaligned time-series data. Extensive experiments across 128 UCR
datasets validate the superiority of our approach over contemporary averaging
methods, including both traditional and learning-based approaches, marking a
significant advancement in the field of time-series analysis.
|
2502.06593
|
A Large-scale AI-generated Image Inpainting Benchmark
|
cs.CV
|
Recent advances in generative models enable highly realistic image
manipulations, creating an urgent need for robust forgery detection methods.
Current datasets for training and evaluating these methods are limited in scale
and diversity. To address this, we propose a methodology for creating
high-quality inpainting datasets and apply it to create DiQuID, comprising over
95,000 inpainted images generated from 78,000 original images sourced from
MS-COCO, RAISE, and OpenImages. Our methodology consists of three components:
(1) Semantically Aligned Object Replacement (SAOR) that identifies suitable
objects through instance segmentation and generates contextually appropriate
prompts, (2) Multiple Model Image Inpainting (MMII) that employs various
state-of-the-art inpainting pipelines primarily based on diffusion models to
create diverse manipulations, and (3) Uncertainty-Guided Deceptiveness
Assessment (UGDA) that evaluates image realism through comparative analysis
with originals. The resulting dataset surpasses existing ones in diversity,
aesthetic quality, and technical quality. We provide comprehensive benchmarking
results using state-of-the-art forgery detection methods, demonstrating the
dataset's effectiveness in evaluating and improving detection algorithms.
Through a human study with 42 participants on 1,000 images, we show that while
humans struggle with images classified as deceiving by our methodology, models
trained on our dataset maintain high performance on these challenging cases.
Code and dataset are available at https://github.com/mever-team/DiQuID.
|
2502.06594
|
A Review of Conceptualizations of Safety and Risk in Current Automated
Driving Regulation
|
eess.SY cs.SY
|
"Safety" and "Risk" are key concepts for the design and development of
automated vehicles. For the market introduction or large-scale field tests,
both concepts are not only relevant for engineers developing the vehicles, but
for all stakeholders (e.g., regulators, lawyers, or the general public) who
have stakes in the technology. In the communication between stakeholder groups,
common notions of these abstract concepts are key for efficient communication
and setting mutual expectations. In the European market, automated vehicles
require Europe-wide type approval or at least operating permits in the
individual states. For this, a central means of communication between
regulators and engineers are regulatory documents. Flawed terminology regarding
the safety expectations for automated vehicles can unnecessarily complicate
relations between regulators and manufacturers, and thus hinder the
introduction of the technology. In this paper, we review relevant documents at
the UN- and EU-level, for the UK, and Germany regarding their (implied) notions
of safety and risk. We contrast the regulatory notions with established and
more recently developing notions of safety and risk in the field of automated
driving. Based on the analysis, we provide recommendations on how explicit
definitions of safety and risk in regulatory documents can support rather than
hinder the market introduction of automated vehicles.
|
2502.06595
|
Surrogate models for diffusion on graphs via sparse polynomials
|
math.NA cs.LG cs.NA
|
Diffusion kernels over graphs have been widely utilized as effective tools in
various applications due to their ability to accurately model the flow of
information through nodes and edges. However, there is a notable gap in the
literature regarding the development of surrogate models for diffusion
processes on graphs. In this work, we fill this gap by proposing sparse
polynomial-based surrogate models for parametric diffusion equations on graphs
with community structure. In tandem, we provide convergence guarantees for both
least squares and compressed sensing-based approximations by showing the
holomorphic regularity of parametric solutions to these diffusion equations.
Our theoretical findings are accompanied by a series of numerical experiments
conducted on both synthetic and real-world graphs that demonstrate the
applicability of our methodology.
|
2502.06597
|
Continual Release Moment Estimation with Differential Privacy
|
cs.LG stat.ML
|
We propose Joint Moment Estimation (JME), a method for continually and
privately estimating both the first and second moments of data with reduced
noise compared to naive approaches. JME uses the matrix mechanism and a joint
sensitivity analysis to allow the second moment estimation with no additional
privacy cost, thereby improving accuracy while maintaining privacy. We
demonstrate JME's effectiveness in two applications: estimating the running
mean and covariance matrix for Gaussian density estimation, and model training
with DP-Adam on CIFAR-10.
|
2502.06599
|
Joint parameter and state estimation for regularized time-discrete
multibody dynamics
|
math.OC cs.SY eess.SY
|
We develop a method for offline parameter estimation of discrete multibody
dynamics with regularized and frictional kinematic constraints. This setting
leads to unobserved degrees of freedom, which we handle using joint state and
parameter estimation. Our method finds the states and parameters as the
solution to a nonlinear least squares optimization problem based on the inverse
dynamics and the observation error. The solution is found using a
Levenberg-Marquardt algorithm with derivatives from automatic differentiation
and custom differentiation rules for the complementary conditions that appear
due to dry frictional constraints. We reduce the number of method parameters to
the choice of the time-step, regularization coefficients, and a parameter that
controls the relative weighting of inverse dynamics and observation errors. We
evaluate the method using synthetic and real measured data, focusing on
performance and sensitivity to method parameters. In particular, we optimize
over a 13-dimensional parameter space, including inertial, frictional, tilt,
and motor parameters, using data from a real Furuta pendulum. Results show fast
convergence, in the order of seconds, and good agreement for different
time-series of recorded data over multiple method parameter choices. However,
very stiff constraints may cause difficulties in solving the optimization
problem. We conclude that our method can be very fast and has method parameters
that are robust and easy to set in the tested scenarios.
|
2502.06600
|
Evaluation of Multilingual Image Captioning: How far can we get with
CLIP models?
|
cs.CL cs.AI
|
The evaluation of image captions, looking at both linguistic fluency and
semantic correspondence to visual contents, has witnessed a significant effort.
Still, despite advancements such as the CLIPScore metric, multilingual
captioning evaluation has remained relatively unexplored. This work presents
several strategies, and extensive experiments, related to evaluating CLIPScore
variants in multilingual settings. To address the lack of multilingual test
data, we consider two different strategies: (1) using quality aware
machine-translated datasets with human judgements, and (2) re-purposing
multilingual datasets that target semantic inference and reasoning. Our results
highlight the potential of finetuned multilingual models to generalize across
languages and to handle complex linguistic challenges. Tests with
machine-translated data show that multilingual CLIPScore models can maintain a
high correlation with human judgements across different languages, and
additional tests with natively multilingual and multicultural data further
attest to the high-quality assessments.
|
2502.06601
|
Amortized In-Context Bayesian Posterior Estimation
|
cs.LG cs.AI stat.ML
|
Bayesian inference provides a natural way of incorporating prior beliefs and
assigning a probability measure to the space of hypotheses. Current solutions
rely on iterative routines like Markov Chain Monte Carlo (MCMC) sampling and
Variational Inference (VI), which need to be re-run whenever new observations
are available. Amortization, through conditional estimation, is a viable
strategy to alleviate such difficulties and has been the guiding principle
behind simulation-based inference, neural processes and in-context methods
using pre-trained models. In this work, we conduct a thorough comparative
analysis of amortized in-context Bayesian posterior estimation methods from the
lens of different optimization objectives and architectural choices. Such
methods train an amortized estimator to perform posterior parameter inference
by conditioning on a set of data examples passed as context to a sequence model
such as a transformer. In contrast to language models, we leverage permutation
invariant architectures as the true posterior is invariant to the ordering of
context examples. Our empirical study includes generalization to
out-of-distribution tasks, cases where the assumed underlying model is
misspecified, and transfer from simulated to real problems. Subsequently, it
highlights the superiority of the reverse KL estimator for predictive problems,
especially when combined with the transformer architecture and normalizing
flows.
|
2502.06604
|
Do we really have to filter out random noise in pre-training data for
language models?
|
cs.CL
|
Web-scale pre-training datasets are the cornerstone of LLMs' success.
However, text data curated from the internet inevitably contains random noise
caused by decoding errors or unregulated web content. In contrast to previous
works that focus on low quality or synthetic data, our study \textbf{provides
the first systematic investigation into such random noise through a cohesive
``What-Why-How'' framework.} Surprisingly, we observed that the resulting
increase in next-token prediction (NTP) loss was significantly lower than the
proportion of random noise. We provide a theoretical justification for this
phenomenon, which also elucidates the success of multilingual models. On the
other hand, experiments show that the model's performance in downstream tasks
is not based solely on the NTP loss, which means that random noise may result
in degraded downstream performance. To address the potential adverse effects,
we introduce a novel plug-and-play Local Gradient Matching loss, which
explicitly enhances the denoising capability of the downstream task head by
aligning the gradient of normal and perturbed features without requiring
knowledge of the model's parameters. Additional experiments on 8 language and
14 vision benchmarks further validate its effectiveness.
|
2502.06606
|
MaterialFusion: High-Quality, Zero-Shot, and Controllable Material
Transfer with Diffusion Models
|
cs.CV
|
Manipulating the material appearance of objects in images is critical for
applications like augmented reality, virtual prototyping, and digital content
creation. We present MaterialFusion, a novel framework for high-quality
material transfer that allows users to adjust the degree of material
application, achieving an optimal balance between new material properties and
the object's original features. MaterialFusion seamlessly integrates the
modified object into the scene by maintaining background consistency and
mitigating boundary artifacts. To thoroughly evaluate our approach, we have
compiled a dataset of real-world material transfer examples and conducted
complex comparative analyses. Through comprehensive quantitative evaluations
and user studies, we demonstrate that MaterialFusion significantly outperforms
existing methods in terms of quality, user control, and background
preservation. Code is available at
https://github.com/ControlGenAI/MaterialFusion.
|
2502.06607
|
Illegal Waste Detection in Remote Sensing Images: A Case Study
|
cs.CV cs.AI
|
Environmental crime currently represents the third largest criminal activity
worldwide while threatening ecosystems as well as human health. Among the
crimes related to this activity, improper waste management can nowadays be
countered more easily thanks to the increasing availability and decreasing cost
of Very-High-Resolution Remote Sensing images, which enable semi-automatic
territory scanning in search of illegal landfills. This paper proposes a
pipeline, developed in collaboration with professionals from a local
environmental agency, for detecting candidate illegal dumping sites leveraging
a classifier of Remote Sensing images. To identify the best configuration for
such classifier, an extensive set of experiments was conducted and the impact
of diverse image characteristics and training settings was thoroughly analyzed.
The local environmental agency was then involved in an experimental exercise
where outputs from the developed classifier were integrated in the experts'
everyday work, resulting in time savings with respect to manual
photo-interpretation. The classifier was eventually run with valuable results
on a location outside of the training area, highlighting potential for
cross-border applicability of the proposed pipeline.
|
2502.06608
|
TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified
Flow Models
|
cs.CV cs.AI
|
Recent advancements in diffusion techniques have propelled image and video
generation to unprecedented levels of quality, significantly accelerating the
deployment and application of generative AI. However, 3D shape generation
technology has so far lagged behind, constrained by limitations in 3D data
scale, complexity of 3D data processing, and insufficient exploration of
advanced techniques in the 3D domain. Current approaches to 3D shape generation
face substantial challenges in terms of output quality, generalization
capability, and alignment with input conditions. We present TripoSG, a new
streamlined shape diffusion paradigm capable of generating high-fidelity 3D
meshes with precise correspondence to input images. Specifically, we propose:
1) A large-scale rectified flow transformer for 3D shape generation, achieving
state-of-the-art fidelity through training on extensive, high-quality data. 2)
A hybrid supervised training strategy combining SDF, normal, and eikonal losses
for 3D VAE, achieving high-quality 3D reconstruction performance. 3) A data
processing pipeline to generate 2 million high-quality 3D samples, highlighting
the crucial rules for data quality and quantity in training 3D generative
models. Through comprehensive experiments, we have validated the effectiveness
of each component in our new framework. The seamless integration of these parts
has enabled TripoSG to achieve state-of-the-art performance in 3D shape
generation. The resulting 3D shapes exhibit enhanced detail due to
high-resolution capabilities and demonstrate exceptional fidelity to input
images. Moreover, TripoSG demonstrates improved versatility in generating 3D
models from diverse image styles and contents, showcasing strong generalization
capabilities. To foster progress and innovation in the field of 3D generation,
we will make our model publicly available.
|
2502.06615
|
Multi-Scale Feature Fusion with Image-Driven Spatial Integration for
Left Atrium Segmentation from Cardiac MRI Images
|
cs.CV eess.IV
|
Accurate segmentation of the left atrium (LA) from late gadolinium-enhanced
magnetic resonance imaging plays a vital role in visualizing diseased atrial
structures, enabling the diagnosis and management of cardiovascular diseases.
It is particularly essential for planning treatment with ablation therapy, a
key intervention for atrial fibrillation (AF). However, manual segmentation is
time-intensive and prone to inter-observer variability, underscoring the need
for automated solutions. Class-agnostic foundation models like DINOv2 have
demonstrated remarkable feature extraction capabilities in vision tasks.
However, their lack of domain specificity and task-specific adaptation can
reduce spatial resolution during feature extraction, impacting the capture of
fine anatomical detail in medical imaging. To address this limitation, we
propose a segmentation framework that integrates DINOv2 as an encoder with a
UNet-style decoder, incorporating multi-scale feature fusion and input image
integration to enhance segmentation accuracy. The learnable weighting mechanism
dynamically prioritizes hierarchical features from different encoder blocks of
the foundation model, optimizing feature selection for task relevance.
Additionally, the input image is reintroduced during the decoding stage to
preserve high-resolution spatial details, addressing limitations of
downsampling in the encoder. We validate our approach on the LAScarQS 2022
dataset and demonstrate improved performance with a 92.3% Dice and 84.1% IoU
score for giant architecture compared to the nnUNet baseline model. These
findings emphasize the efficacy of our approach in advancing the field of
automated left atrium segmentation from cardiac MRI.
|
2502.06617
|
Scaling Multi-Document Event Summarization: Evaluating Compression vs.
Full-Text Approaches
|
cs.CL
|
Automatically summarizing large text collections is a valuable tool for
document research, with applications in journalism, academic research, legal
work, and many other fields. In this work, we contrast two classes of systems
for large-scale multi-document summarization (MDS): compression and full-text.
Compression-based methods use a multi-stage pipeline and often lead to lossy
summaries. Full-text methods promise a lossless summary by relying on recent
advances in long-context reasoning. To understand their utility on large-scale
MDS, we evaluated them on three datasets, each containing approximately one
hundred documents per summary. Our experiments cover a diverse set of
long-context transformers (Llama-3.1, Command-R, Jamba-1.5-Mini) and
compression methods (retrieval-augmented, hierarchical, incremental). Overall,
we find that full-text and retrieval methods perform the best in most settings.
With further analysis into the salient information retention patterns, we show
that compression-based methods show strong promise at intermediate stages, even
outperforming full-context. However, they suffer information loss due to their
multi-stage pipeline and lack of global context. Our results highlight the need
to develop hybrid approaches that combine compression and full-text approaches
for optimal performance on large-scale multi-document summarization.
|
2502.06618
|
On the Reliability of Information Retrieval From MDS Coded Data in DNA
Storage
|
cs.IT cs.ET math.IT
|
This work presents a theoretical analysis of the probability of successfully
retrieving data encoded with MDS codes (e.g., Reed-Solomon codes) in DNA
storage systems. We study this probability under independent and identically
distributed (i.i.d.) substitution errors, focusing on a common code design
strategy that combines inner and outer MDS codes. Our analysis demonstrates how
this probability depends on factors such as the total number of sequencing
reads, their distribution across strands, the rates of the inner and outer
codes, and the substitution error probabilities. These results provide
actionable insights into optimizing DNA storage systems under reliability
constraints, including determining the minimum number of sequencing reads
needed for reliable data retrieval and identifying the optimal balance between
the rates of inner and outer MDS codes.
|
2502.06619
|
Unleashing the Potential of Pre-Trained Diffusion Models for
Generalizable Person Re-Identification
|
cs.CV
|
Domain-generalizable re-identification (DG Re-ID) aims to train a model on
one or more source domains and evaluate its performance on unseen target
domains, a task that has attracted growing attention due to its practical
relevance. While numerous methods have been proposed, most rely on
discriminative or contrastive learning frameworks to learn generalizable
feature representations. However, these approaches often fail to mitigate
shortcut learning, leading to suboptimal performance. In this work, we propose
a novel method called diffusion model-assisted representation learning with a
correlation-aware conditioning scheme (DCAC) to enhance DG Re-ID. Our method
integrates a discriminative and contrastive Re-ID model with a pre-trained
diffusion model through a correlation-aware conditioning scheme. By
incorporating ID classification probabilities generated from the Re-ID model
with a set of learnable ID-wise prompts, the conditioning scheme injects dark
knowledge that captures ID correlations to guide the diffusion process.
Simultaneously, feedback from the diffusion model is back-propagated through
the conditioning scheme to the Re-ID model, effectively improving the
generalization capability of Re-ID features. Extensive experiments on both
single-source and multi-source DG Re-ID tasks demonstrate that our method
achieves state-of-the-art performance. Comprehensive ablation studies further
validate the effectiveness of the proposed approach, providing insights into
its robustness. Codes will be available at https://github.com/RikoLi/DCAC.
|
2502.06627
|
Towards Closing the Gap between Model-Based Systems Engineering and
Automated Vehicle Assurance: Tailoring Generic Methods by Integrating Domain
Knowledge
|
eess.SY cs.SY
|
Designing, assuring and releasing safe automated vehicles is a highly
interdisciplinary process. As complex systems, automated driving systems will
inevitably be subject to emergent properties, i. e., the properties of the
overall system will be more than just a sum of the properties of its integrated
elements. Safety is one example of such emergent properties. In this regard, it
must be ensured that effects of emergence do not render an overall system that
is composed of safety-approved sub systems unsafe. The key challenges in this
regard are twofold: Regarding the interdisciplinary character of the
development and assurance processes, all relevant stakeholders must speak a
common language and have a common understanding of the key concepts that
influence system safety. Additionally, the individual properties of system
elements should remain traceable to the system level. Model-Based Systems
Engineering (MBSE) provides an interdisciplinary mindset, as well as methods
and processes to manage emergent system properties over the entire system
lifecycle. By this, MBSE provides tools that can assist the assurance process
for automated vehicles. However, concepts from the domain of MBSE have a
reputation for not being directly accessible for domain experts who are no
experts in the field of Systems Engineering. This paper highlights challenges
when applying MBSE methods to the design and development of automated driving
systems. It will present an approach to create and apply domain-specific SysML
profiles, which can be a first step for enhancing communication between
different stake-holders in the development and safety assurance processes.
|
2502.06631
|
Conformal Predictions for Human Action Recognition with Vision-Language
Models
|
cs.CV cs.AI
|
Human-In-The-Loop (HITL) frameworks are integral to many real-world computer
vision systems, enabling human operators to make informed decisions with AI
assistance. Conformal Predictions (CP), which provide label sets with rigorous
guarantees on ground truth inclusion probabilities, have recently gained
traction as a valuable tool in HITL settings. One key application area is video
surveillance, closely associated with Human Action Recognition (HAR). This
study explores the application of CP on top of state-of-the-art HAR methods
that utilize extensively pre-trained Vision-Language Models (VLMs). Our
findings reveal that CP can significantly reduce the average number of
candidate classes without modifying the underlying VLM. However, these
reductions often result in distributions with long tails. To address this, we
introduce a method based on tuning the temperature parameter of the VLMs to
minimize these tails without requiring additional calibration data. Our code is
made available on GitHub at the address https://github.com/tbary/CP4VLM.
|
2502.06632
|
Few-Shot Classification and Anatomical Localization of Tissues in SPECT
Imaging
|
cs.CV cs.AI cs.LG
|
Accurate classification and anatomical localization are essential for
effective medical diagnostics and research, which may be efficiently performed
using deep learning techniques. However, availability of limited labeled data
poses a significant challenge. To address this, we adapted Prototypical
Networks and the Propagation-Reconstruction Network (PRNet) for few-shot
classification and localization, respectively, in Single Photon Emission
Computed Tomography (SPECT) images. For the proof of concept we used a
2D-sliced image cropped around heart. The Prototypical Network, with a
pre-trained ResNet-18 backbone, classified ventricles, myocardium, and liver
tissues with 96.67% training and 93.33% validation accuracy. PRNet, adapted for
2D imaging with an encoder-decoder architecture and skip connections, achieved
a training loss of 1.395, accurately reconstructing patches and capturing
spatial relationships. These results highlight the potential of Prototypical
Networks for tissue classification with limited labeled data and PRNet for
anatomical landmark localization, paving the way for improved performance in
deep learning frameworks.
|
2502.06633
|
Combining Large Language Models with Static Analyzers for Code Review
Generation
|
cs.SE cs.AI
|
Code review is a crucial but often complex, subjective, and time-consuming
activity in software development. Over the past decades, significant efforts
have been made to automate this process. Early approaches focused on
knowledge-based systems (KBS) that apply rule-based mechanisms to detect code
issues, providing precise feedback but struggling with complex,
context-dependent cases. More recent work has shifted toward fine-tuning
pre-trained language models for code review, enabling broader issue coverage
but often at the expense of precision. In this paper, we propose a hybrid
approach that combines the strengths of KBS and learning-based systems (LBS) to
generate high-quality, comprehensive code reviews. Our method integrates
knowledge at three distinct stages of the language model pipeline: during data
preparation (Data-Augmented Training, DAT), at inference (Retrieval-Augmented
Generation, RAG), and after inference (Naive Concatenation of Outputs, NCO). We
empirically evaluate our combination strategies against standalone KBS and LBS
fine-tuned on a real-world dataset. Our results show that these hybrid
strategies enhance the relevance, completeness, and overall quality of review
comments, effectively bridging the gap between rule-based tools and deep
learning models.
|
2502.06634
|
Automatic Annotation Augmentation Boosts Translation between Molecules
and Natural Language
|
cs.LG cs.AI q-bio.BM
|
Recent advancements in AI for biological research focus on integrating
molecular data with natural language to accelerate drug discovery. However, the
scarcity of high-quality annotations limits progress in this area. This paper
introduces LA$^3$, a Language-based Automatic Annotation Augmentation framework
that leverages large language models to augment existing datasets, thereby
improving AI training. We demonstrate the effectiveness of LA$^3$ by creating
an enhanced dataset, LaChEBI-20, where we systematically rewrite the
annotations of molecules from an established dataset. These rewritten
annotations preserve essential molecular information while providing more
varied sentence structures and vocabulary. Using LaChEBI-20, we train LaMolT5
based on a benchmark architecture to learn the mapping between molecular
representations and augmented annotations.
Experimental results on text-based *de novo* molecule generation and molecule
captioning demonstrate that LaMolT5 outperforms state-of-the-art models.
Notably, incorporating LA$^3$ leads to improvements of up to 301% over the
benchmark architecture. Furthermore, we validate the effectiveness of LA$^3$
notable applications in *image*, *text* and *graph* tasks, affirming its
versatility and utility.
|
2502.06635
|
Steel-LLM:From Scratch to Open Source -- A Personal Journey in Building
a Chinese-Centric LLM
|
cs.CL cs.AI
|
Steel-LLM is a Chinese-centric language model developed from scratch with the
goal of creating a high-quality, open-source model despite limited
computational resources. Launched in March 2024, the project aimed to train a
1-billion-parameter model on a large-scale dataset, prioritizing transparency
and the sharing of practical insights to assist others in the community. The
training process primarily focused on Chinese data, with a small proportion of
English data included, addressing gaps in existing open-source LLMs by
providing a more detailed and practical account of the model-building journey.
Steel-LLM has demonstrated competitive performance on benchmarks such as CEVAL
and CMMLU, outperforming early models from larger institutions. This paper
provides a comprehensive summary of the project's key contributions, including
data collection, model design, training methodologies, and the challenges
encountered along the way, offering a valuable resource for researchers and
practitioners looking to develop their own LLMs. The model checkpoints and
training script are available at https://github.com/zhanshijinwat/Steel-LLM.
|
2502.06636
|
Enhancing healthcare infrastructure resilience through agent-based
simulation methods
|
cs.MA cs.SY eess.SY
|
Critical infrastructures face demanding challenges due to natural and
human-generated threats, such as pandemics, workforce shortages or
cyber-attacks, which might severely compromise service quality. To improve
system resilience, decision-makers would need intelligent tools for quick and
efficient resource allocation. This article explores an agent-based simulation
model that intends to capture a part of the complexity of critical
infrastructure systems, particularly considering the interdependencies of
healthcare systems with information and telecommunication systems. Such a model
enables to implement a simulation-based optimization approach in which the
exposure of critical systems to risks is evaluated, while comparing the
mitigation effects of multiple tactical and strategical decision alternatives
to enhance their resilience. The proposed model is designed to be
parameterizable, to enable adapting it to risk scenarios with different
severity, and it facilitates the compilation of relevant performance indicators
enabling monitoring at both agent level and system level. To validate the
agent-based model, a literature-supported methodology has been used to perform
cross-validation, sensitivity analysis and test the usefulness of the proposed
model through a use case. The use case analyzes the impact of a concurrent
pandemic and a cyber-attack on a hospital and compares different
resiliency-enhancing countermeasures using contingency tables. Overall, the use
case illustrates the feasibility and versatility of the proposed approach to
enhance resiliency.
|
2502.06643
|
MoETuner: Optimized Mixture of Expert Serving with Balanced Expert
Placement and Token Routing
|
cs.LG cs.DC
|
Mixture-of-Experts (MoE) model architecture has emerged as a promising
solution for scaling transformer models efficiently, offering sparse activation
that reduces computational costs while increasing model capacity. However, as
MoE models scale, they need to be distributed across GPU devices, thus face
critical performance bottlenecks due to their large memory footprint. Expert
parallelism distributes experts across GPUs, however, faces key challenges
including an unbalanced token routing and expert activation, resulting in
communication tail latency and processing inefficiencies. While existing
solutions address some of these issues, they fail to resolve the dual
challenges of load imbalance and communication skew. The imbalance in token
processing load across experts causes uneven processing times on different
GPUs, while communication skew between GPUs leads to unbalanced inter-GPU data
transfers. These factors degrade the performance of MoE models by increasing
tail latency and reducing overall throughput. To address these limitations, we
propose an Integer Linear Programming (ILP) formulation to optimize expert
placement by jointly considering token load, communication, and computation
costs. We exploit the property that there is a token routing dependency across
layers, where tokens routed to a specific expert in one layer are likely to be
routed to a limited set of experts in the subsequent layer. Our solution,
MoETuner, offers an optimal expert-to-GPU assignment that minimizes inter-GPU
token routing costs and balances token processing across devices, thereby
reducing tail latency and end-to-end execution time. Experimental results
demonstrate 9.3% and 17.5% of end-to-end speedups for single-node and
multi-node inference respectively, showcasing the potential of our ILP-based
optimization for offering expert parallel solutions for next-generation MoEs.
|
2502.06645
|
Koopman-Equivariant Gaussian Processes
|
cs.LG cs.SY eess.SY stat.ML
|
Credible forecasting and representation learning of dynamical systems are of
ever-increasing importance for reliable decision-making. To that end, we
propose a family of Gaussian processes (GP) for dynamical systems with linear
time-invariant responses, which are nonlinear only in initial conditions. This
linearity allows us to tractably quantify forecasting and representational
uncertainty, simultaneously alleviating the challenge of computing the
distribution of trajectories from a GP-based dynamical system and enabling a
new probabilistic treatment of learning Koopman operator representations. Using
a trajectory-based equivariance -- which we refer to as \textit{Koopman
equivariance} -- we obtain a GP model with enhanced generalization
capabilities. To allow for large-scale regression, we equip our framework with
variational inference based on suitable inducing points. Experiments
demonstrate on-par and often better forecasting performance compared to
kernel-based methods for learning dynamical systems.
|
2502.06648
|
The 2021 Tokyo Olympics Multilingual News Article Dataset
|
cs.IR cs.AI cs.CL
|
In this paper, we introduce a dataset of multilingual news articles covering
the 2021 Tokyo Olympics. A total of 10,940 news articles were gathered from
1,918 different publishers, covering 1,350 sub-events of the 2021 Olympics, and
published between July 1, 2021, and August 14, 2021. These articles are written
in nine languages from different language families and in different scripts. To
create the dataset, the raw news articles were first retrieved via a service
that collects and analyzes news articles. Then, the articles were grouped using
an online clustering algorithm, with each group containing articles reporting
on the same sub-event. Finally, the groups were manually annotated and
evaluated. The development of this dataset aims to provide a resource for
evaluating the performance of multilingual news clustering algorithms, for
which limited datasets are available. It can also be used to analyze the
dynamics and events of the 2021 Tokyo Olympics from different perspectives. The
dataset is available in CSV format and can be accessed from the CLARIN.SI
repository.
|
2502.06649
|
Estimation of Food Intake Quantity Using Inertial Signals from
Smartwatches
|
eess.SP cs.LG
|
Accurate monitoring of eating behavior is crucial for managing obesity and
eating disorders such as bulimia nervosa. At the same time, existing methods
rely on multiple and/or specialized sensors, greatly harming adherence and
ultimately, the quality and continuity of data. This paper introduces a novel
approach for estimating the weight of a bite, from a commercial smartwatch. Our
publicly-available dataset contains smartwatch inertial data from ten
participants, with manually annotated start and end times of each bite along
with their corresponding weights from a smart scale, under semi-controlled
conditions. The proposed method combines extracted behavioral features such as
the time required to load the utensil with food, with statistical features of
inertial signals, that serve as input to a Support Vector Regression model to
estimate bite weights. Under a leave-one-subject-out cross-validation scheme,
our approach achieves a mean absolute error (MAE) of 3.99 grams per bite. To
contextualize this performance, we introduce the improvement metric, that
measures the relative MAE difference compared to a baseline model. Our method
demonstrates a 17.41% improvement, while the adapted state-of-the art method
shows a -28.89% performance against that same baseline. The results presented
in this work establish the feasibility of extracting meaningful bite weight
estimates from commercial smartwatch inertial sensors alone, laying the
groundwork for future accessible, non-invasive dietary monitoring systems.
|
2502.06650
|
Prototype Contrastive Consistency Learning for Semi-Supervised Medical
Image Segmentation
|
cs.CV
|
Medical image segmentation is a crucial task in medical image analysis, but
it can be very challenging especially when there are less labeled data but with
large unlabeled data. Contrastive learning has proven to be effective for
medical image segmentation in semi-supervised learning by constructing
contrastive samples from partial pixels. However, although previous contrastive
learning methods can mine semantic information from partial pixels within
images, they ignore the whole context information of unlabeled images, which is
very important to precise segmentation. In order to solve this problem, we
propose a novel prototype contrastive learning method called Prototype
Contrastive Consistency Segmentation (PCCS) for semi-supervised medical image
segmentation. The core idea is to enforce the prototypes of the same semantic
class to be closer and push the prototypes in different semantic classes far
away from each other. Specifically, we construct a signed distance map and an
uncertainty map from unlabeled images. The signed distance map is used to
construct prototypes for contrastive learning, and then we estimate the
prototype uncertainty from the uncertainty map as trade-off among prototypes.
In order to obtain better prototypes, based on the student-teacher
architecture, a new mechanism named prototype updating prototype is designed to
assist in updating the prototypes for contrastive learning. In addition, we
propose an uncertainty-consistency loss to mine more reliable information from
unlabeled data. Extensive experiments on medical image segmentation demonstrate
that PCCS achieves better segmentation performance than the state-of-the-art
methods. The code is available at https://github.com/comphsh/PCCS.
|
2502.06652
|
Transparent NLP: Using RAG and LLM Alignment for Privacy Q&A
|
cs.CL
|
The transparency principle of the General Data Protection Regulation (GDPR)
requires data processing information to be clear, precise, and accessible.
While language models show promise in this context, their probabilistic nature
complicates truthfulness and comprehensibility.
This paper examines state-of-the-art Retrieval Augmented Generation (RAG)
systems enhanced with alignment techniques to fulfill GDPR obligations. We
evaluate RAG systems incorporating an alignment module like Rewindable
Auto-regressive Inference (RAIN) and our proposed multidimensional extension,
MultiRAIN, using a Privacy Q&A dataset. Responses are optimized for preciseness
and comprehensibility and are assessed through 21 metrics, including
deterministic and large language model-based evaluations.
Our results show that RAG systems with an alignment module outperform
baseline RAG systems on most metrics, though none fully match human answers.
Principal component analysis of the results reveals complex interactions
between metrics, highlighting the need to refine metrics. This study provides a
foundation for integrating advanced natural language processing systems into
legal compliance frameworks.
|
2502.06653
|
In-Context Learning (and Unlearning) of Length Biases
|
cs.CL
|
Large language models have demonstrated strong capabilities to learn
in-context, where exemplar input-output pairings are appended to the prompt for
demonstration. However, existing work has demonstrated the ability of models to
learn lexical and label biases in-context, which negatively impacts both
performance and robustness of models. The impact of other statistical data
biases remains under-explored, which this work aims to address. We specifically
investigate the impact of length biases on in-context learning. We demonstrate
that models do learn length biases in the context window for their predictions,
and further empirically analyze the factors that modulate the level of bias
exhibited by the model. In addition, we show that learning length information
in-context can be used to counter the length bias that has been encoded in
models (e.g., via fine-tuning). This reveals the power of in-context learning
in debiasing model prediction behaviors without the need for costly parameter
updates.
|
2502.06655
|
Unbiased Evaluation of Large Language Models from a Causal Perspective
|
cs.AI
|
Benchmark contamination has become a significant concern in the LLM
evaluation community. Previous Agents-as-an-Evaluator address this issue by
involving agents in the generation of questions. Despite their success, the
biases in Agents-as-an-Evaluator methods remain largely unexplored. In this
paper, we present a theoretical formulation of evaluation bias, providing
valuable insights into designing unbiased evaluation protocols. Furthermore, we
identify two type of bias in Agents-as-an-Evaluator through carefully designed
probing tasks on a minimal Agents-as-an-Evaluator setup. To address these
issues, we propose the Unbiased Evaluator, an evaluation protocol that delivers
a more comprehensive, unbiased, and interpretable assessment of LLMs.Extensive
experiments reveal significant room for improvement in current LLMs.
Additionally, we demonstrate that the Unbiased Evaluator not only offers strong
evidence of benchmark contamination but also provides interpretable evaluation
results.
|
2502.06656
|
A Frontier AI Risk Management Framework: Bridging the Gap Between
Current AI Practices and Established Risk Management
|
cs.AI
|
The recent development of powerful AI systems has highlighted the need for
robust risk management frameworks in the AI industry. Although companies have
begun to implement safety frameworks, current approaches often lack the
systematic rigor found in other high-risk industries. This paper presents a
comprehensive risk management framework for the development of frontier AI that
bridges this gap by integrating established risk management principles with
emerging AI-specific practices. The framework consists of four key components:
(1) risk identification (through literature review, open-ended red-teaming, and
risk modeling), (2) risk analysis and evaluation using quantitative metrics and
clearly defined thresholds, (3) risk treatment through mitigation measures such
as containment, deployment controls, and assurance processes, and (4) risk
governance establishing clear organizational structures and accountability.
Drawing from best practices in mature industries such as aviation or nuclear
power, while accounting for AI's unique challenges, this framework provides AI
developers with actionable guidelines for implementing robust risk management.
The paper details how each component should be implemented throughout the
life-cycle of the AI system - from planning through deployment - and emphasizes
the importance and feasibility of conducting risk management work prior to the
final training run to minimize the burden associated with it.
|
2502.06658
|
Generating Samples to Question Trained Models
|
cs.LG
|
There is a growing need for investigating how machine learning models
operate. With this work, we aim to understand trained machine learning models
by questioning their data preferences. We propose a mathematical framework that
allows us to probe trained models and identify their preferred samples in
various scenarios including prediction-risky, parameter-sensitive, or
model-contrastive samples. To showcase our framework, we pose these queries to
a range of models trained on a range of classification and regression tasks,
and receive answers in the form of generated data.
|
2502.06659
|
Who Taught You That? Tracing Teachers in Model Distillation
|
cs.CL
|
Model distillation -- using outputs from a large teacher model to teach a
small student model -- is a practical means of creating efficient models for a
particular task. We ask: Can we identify a students' teacher based on its
outputs? Such "footprints" left by teacher LLMs would be interesting artifacts.
Beyond this, reliable teacher inference may have practical implications as
actors seek to distill specific capabilities of massive proprietary LLMs into
deployed smaller LMs, potentially violating terms of service. We consider
practical task distillation targets including summarization, question
answering, and instruction-following. We assume a finite set of candidate
teacher models, which we treat as blackboxes. We design discriminative models
that operate over lexical features. We find that $n$-gram similarity alone is
unreliable for identifying teachers, but part-of-speech (PoS) templates
preferred by student models mimic those of their teachers.
|
2502.06661
|
iLOCO: Distribution-Free Inference for Feature Interactions
|
stat.ML cs.LG
|
Feature importance measures are widely studied and are essential for
understanding model behavior, guiding feature selection, and enhancing
interpretability. However, many machine learning fitted models involve complex,
higher-order interactions between features. Existing feature importance metrics
fail to capture these higher-order effects while existing interaction metrics
often suffer from limited applicability or excessive computation; no methods
exist to conduct statistical inference for feature interactions. To bridge this
gap, we first propose a new model-agnostic metric, interaction
Leave-One-Covariate-Out iLOCO, for measuring the importance of higher-order
feature interactions. Next, we leverage recent advances in LOCO inference to
develop distribution-free and assumption-light confidence intervals for our
iLOCO metric. To address computational challenges, we also introduce an
ensemble learning method for calculating the iLOCO metric and confidence
intervals that we show is both computationally and statistically efficient. We
validate our iLOCO metric and our confidence intervals on both synthetic and
real data sets, showing that our approach outperforms existing methods and
provides the first inferential approach to detecting feature interactions.
|
2502.06663
|
EfficientLLM: Scalable Pruning-Aware Pretraining for
Architecture-Agnostic Edge Language Models
|
cs.LG
|
Modern large language models (LLMs) driven by scaling laws, achieve
intelligence emergency in large model sizes. Recently, the increasing concerns
about cloud costs, latency, and privacy make it an urgent requirement to
develop compact edge language models. Distinguished from direct pretraining
that bounded by the scaling law, this work proposes the pruning-aware
pretraining, focusing on retaining performance of much larger optimized models.
It features following characteristics: 1) Data-scalable: we introduce minimal
parameter groups in LLM and continuously optimize structural pruning, extending
post-training pruning methods like LLM-Pruner and SparseGPT into the
pretraining phase. 2) Architecture-agnostic: the LLM architecture is
auto-designed using saliency-driven pruning, which is the first time to exceed
SoTA human-designed LLMs in modern pretraining. We reveal that it achieves
top-quality edge language models, termed EfficientLLM, by scaling up LLM
compression and extending its boundary. EfficientLLM significantly outperforms
SoTA baselines with $100M \sim 1B$ parameters, such as MobileLLM, SmolLM,
Qwen2.5-0.5B, OLMo-1B, Llama3.2-1B in common sense benchmarks. As the first
attempt, EfficientLLM bridges the performance gap between traditional LLM
compression and direct pretraining methods, and we will fully open source at
https://github.com/Xingrun-Xing2/EfficientLLM.
|
2502.06664
|
Evaluation of Deep Audio Representations for Hearables
|
cs.SD cs.AI cs.LG
|
Effectively steering hearable devices requires understanding the acoustic
environment around the user. In the computational analysis of sound scenes,
foundation models have emerged as the state of the art to produce
high-performance, robust, multi-purpose audio representations. We introduce and
release Deep Evaluation of Audio Representations (DEAR), the first dataset and
benchmark to evaluate the efficacy of foundation models in capturing essential
acoustic properties for hearables. The dataset includes 1,158 audio tracks,
each 30 seconds long, created by spatially mixing proprietary monologues with
commercial, high-quality recordings of everyday acoustic scenes. Our benchmark
encompasses eight tasks that assess the general context, speech sources, and
technical acoustic properties of the audio scenes. Through our evaluation of
four general-purpose audio representation models, we demonstrate that the BEATs
model significantly surpasses its counterparts. This superiority underscores
the advantage of models trained on diverse audio collections, confirming their
applicability to a wide array of auditory tasks, including encoding the
environment properties necessary for hearable steering. The DEAR dataset and
associated code are available at https://dear-dataset.github.io.
|
2502.06666
|
Automatic Evaluation of Healthcare LLMs Beyond Question-Answering
|
cs.CL cs.AI
|
Current Large Language Models (LLMs) benchmarks are often based on open-ended
or close-ended QA evaluations, avoiding the requirement of human labor.
Close-ended measurements evaluate the factuality of responses but lack
expressiveness. Open-ended capture the model's capacity to produce discourse
responses but are harder to assess for correctness. These two approaches are
commonly used, either independently or together, though their relationship
remains poorly understood. This work is focused on the healthcare domain, where
both factuality and discourse matter greatly. It introduces a comprehensive,
multi-axis suite for healthcare LLM evaluation, exploring correlations between
open and close benchmarks and metrics. Findings include blind spots and
overlaps in current methodologies. As an updated sanity check, we release a new
medical benchmark --CareQA-- with both open and closed variants. Finally, we
propose a novel metric for open-ended evaluations -- Relaxed Perplexity -- to
mitigate the identified limitations.
|
2502.06669
|
Boosting Self-Efficacy and Performance of Large Language Models via
Verbal Efficacy Stimulations
|
cs.CL cs.AI
|
Significant improvements have been observed in the zero-shot capabilities of
the Large Language Models (LLMs). Due to their high sensitivity to input,
research has increasingly focused on enhancing LLMs' performance via direct and
simple prompt engineering rather than intricate domain adaptation. Studies
suggest that LLMs exhibit emotional intelligence, and both positive and
negative emotions can potentially enhance task performances. However, prior
interaction prompts have predominantly concentrated on a single stimulus type,
neglecting to compare different stimulus effects, examine the influence of
varying task difficulties, or explore underlying mechanisms. This paper,
inspired by the positive correlation between self-efficacy and task performance
within the social cognitive theory, introduces Verbal Efficacy Stimulations
(VES). Our VES comprises three types of verbal prompts: encouraging,
provocative, and critical, addressing six aspects such as helpfulness and
competence. And we further categorize task difficulty, aiming to extensively
investigate how distinct VES influence the self-efficacy and task achievements
of language models at varied levels of difficulty. The experimental results
show that the three types of VES improve the performance of LLMs on most tasks,
and the most effective VES varies for different models. In extensive
experiments, we have obtained some findings consistent with psychological
theories, providing novel insights for future research.
|
2502.06673
|
Selecting Optimal Sampling Rate for Stable Super-Resolution
|
math.NA cs.IT cs.NA math.IT
|
We investigate the recovery of nodes and amplitudes from noisy frequency
samples in spike train signals, also known as the super-resolution (SR)
problem. When the node separation falls below the Rayleigh limit, the problem
becomes ill-conditioned. Admissible sampling rates, or decimation parameters,
improve the conditioning of the SR problem, enabling more accurate recovery. We
propose an efficient preprocessing method to identify the optimal sampling
rate, significantly enhancing the performance of SR techniques.
|
2502.06674
|
RAILS: Risk-Aware Iterated Local Search for Joint SLA Decomposition and
Service Provider Management in Multi-Domain Networks
|
cs.NI cs.LG
|
The emergence of the fifth generation (5G) technology has transformed mobile
networks into multi-service environments, necessitating efficient network
slicing to meet diverse Service Level Agreements (SLAs). SLA decomposition
across multiple network domains, each potentially managed by different service
providers, poses a significant challenge due to limited visibility into
real-time underlying domain conditions. This paper introduces Risk-Aware
Iterated Local Search (RAILS), a novel risk model-driven meta-heuristic
framework designed to jointly address SLA decomposition and service provider
selection in multi-domain networks. By integrating online risk modeling with
iterated local search principles, RAILS effectively navigates the complex
optimization landscape, utilizing historical feedback from domain controllers.
We formulate the joint problem as a Mixed-Integer Nonlinear Programming (MINLP)
problem and prove its NP-hardness. Extensive simulations demonstrate that RAILS
achieves near-optimal performance, offering an efficient, real-time solution
for adaptive SLA management in modern multi-domain networks.
|
2502.06676
|
Discovery of skill switching criteria for learning agile quadruped
locomotion
|
cs.RO
|
This paper develops a hierarchical learning and optimization framework that
can learn and achieve well-coordinated multi-skill locomotion. The learned
multi-skill policy can switch between skills automatically and naturally in
tracking arbitrarily positioned goals and recover from failures promptly. The
proposed framework is composed of a deep reinforcement learning process and an
optimization process. First, the contact pattern is incorporated into the
reward terms for learning different types of gaits as separate policies without
the need for any other references. Then, a higher level policy is learned to
generate weights for individual policies to compose multi-skill locomotion in a
goal-tracking task setting. Skills are automatically and naturally switched
according to the distance to the goal. The proper distances for skill switching
are incorporated in reward calculation for learning the high level policy and
updated by an outer optimization loop as learning progresses. We first
demonstrated successful multi-skill locomotion in comprehensive tasks on a
simulated Unitree A1 quadruped robot. We also deployed the learned policy in
the real world showcasing trotting, bounding, galloping, and their natural
transitions as the goal position changes. Moreover, the learned policy can
react to unexpected failures at any time, perform prompt recovery, and resume
locomotion successfully. Compared to discrete switch between single skills
which failed to transition to galloping in the real world, our proposed
approach achieves all the learned agile skills, with smoother and more
continuous skill transitions.
|
2502.06678
|
Quantile Multi-Armed Bandits with 1-bit Feedback
|
stat.ML cs.IT cs.LG math.IT
|
In this paper, we study a variant of best-arm identification involving
elements of risk sensitivity and communication constraints. Specifically, the
goal of the learner is to identify the arm with the highest quantile reward,
while the communication from an agent (who observes rewards) and the learner
(who chooses actions) is restricted to only one bit of feedback per arm pull.
We propose an algorithm that utilizes noisy binary search as a subroutine,
allowing the learner to estimate quantile rewards through 1-bit feedback. We
derive an instance-dependent upper bound on the sample complexity of our
algorithm and provide an algorithm-independent lower bound for specific
instances, with the two matching to within logarithmic factors under mild
conditions, or even to within constant factors in certain low error probability
scaling regimes. The lower bound is applicable even in the absence of
communication constraints, and thus we conclude that restricting to 1-bit
feedback has a minimal impact on the scaling of the sample complexity.
|
2502.06681
|
CHIRLA: Comprehensive High-resolution Identification and
Re-identification for Large-scale Analysis
|
cs.CV cs.AI cs.LG
|
Person re-identification (Re-ID) is a key challenge in computer vision,
requiring the matching of individuals across different cameras, locations, and
time periods. While most research focuses on short-term scenarios with minimal
appearance changes, real-world applications demand robust Re-ID systems capable
of handling long-term scenarios, where persons' appearances can change
significantly due to variations in clothing and physical characteristics. In
this paper, we present CHIRLA, Comprehensive High-resolution Identification and
Re-identification for Large-scale Analysis, a novel dataset specifically
designed for long-term person Re-ID. CHIRLA consists of recordings from
strategically placed cameras over a seven-month period, capturing significant
variations in both temporal and appearance attributes, including controlled
changes in participants' clothing and physical features. The dataset includes
22 individuals, four connected indoor environments, and seven cameras. We
collected more than five hours of video that we semi-automatically labeled to
generate around one million bounding boxes with identity annotations. By
introducing this comprehensive benchmark, we aim to facilitate the development
and evaluation of Re-ID algorithms that can reliably perform in challenging,
long-term real-world scenarios.
|
2502.06682
|
Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint
in a Driving Scene
|
cs.CV
|
Self-driving cars relying solely on ego-centric perception face limitations
in sensing, often failing to detect occluded, faraway objects. Collaborative
autonomous driving (CAV) seems like a promising direction, but collecting data
for development is non-trivial. It requires placing multiple sensor-equipped
agents in a real-world driving scene, simultaneously! As such, existing
datasets are limited in locations and agents. We introduce a novel surrogate to
the rescue, which is to generate realistic perception from different viewpoints
in a driving scene, conditioned on a real-world sample - the ego-car's sensory
data. This surrogate has huge potential: it could potentially turn any ego-car
dataset into a collaborative driving one to scale up the development of CAV. We
present the very first solution, using a combination of simulated collaborative
data and real ego-car data. Our method, Transfer Your Perspective (TYP), learns
a conditioned diffusion model whose output samples are not only realistic but
also consistent in both semantics and layouts with the given ego-car data.
Empirical results demonstrate TYP's effectiveness in aiding in a CAV setting.
In particular, TYP enables us to (pre-)train collaborative perception
algorithms like early and late fusion with little or no real-world
collaborative data, greatly facilitating downstream CAV applications.
|
2502.06683
|
Solving Optimal Power Flow on a Data-Budget: Feature Selection on Smart
Meter Data
|
eess.SY cs.SY
|
How much data is needed to optimally schedule distributed energy resources
(DERs)? Does the distribution system operator (DSO) have to precisely know load
demands and solar injections at each bus of the feeder to solve an optimal
power flow (OPF)? This work exploits redundancies in OPF's structure and data
to avoid communicating such data deluge, and explores the trade-off between
data compression and grid's performance. We propose an OPF data distillation
framework involving two steps. The DSO first collects OPF data from only a
subset of nodes. The DSO subsequently reconstructs the complete OPF data from
the partial ones, and feeds them into the OPF solver. Selecting and
reconstructing OPF data may be performed to either maximize the fidelity of
reconstructed OPF data, or maximize the fidelity of OPF solutions corresponding
to reconstructed data. Under the first objective, OPF data distillation is
posed as a sparsity-regularized convex problem. Under the second objective, it
is posed as a sparsity-regularized bilevel program. Both problems are solved
using accelerated proximal gradient (PGD) algorithms. Numerical tests
corroborate that the bilevel formulation enhances fidelity and feasibility of
reconstructed OPF solutions, and that OPF solutions can be approximated
reasonably well even from partial data.
|
2502.06684
|
EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks
|
cs.LG cs.AI
|
Recent foundational models for tabular data, such as TabPFN, have
demonstrated remarkable effectiveness in adapting to new tasks through
in-context learning. However, these models overlook a crucial equivariance
property: the arbitrary ordering of target dimensions should not influence
model predictions. In this study, we identify this oversight as a source of
incompressible error, termed the equivariance gap, which introduces instability
in predictions. To mitigate these issues, we propose a novel model designed to
preserve equivariance across output dimensions. Our experimental results
indicate that our proposed model not only addresses these pitfalls effectively
but also achieves competitive benchmark performance.
|
2502.06685
|
No Trick, No Treat: Pursuits and Challenges Towards Simulation-free
Training of Neural Samplers
|
cs.LG stat.ML
|
We consider the sampling problem, where the aim is to draw samples from a
distribution whose density is known only up to a normalization constant. Recent
breakthroughs in generative modeling to approximate a high-dimensional data
distribution have sparked significant interest in developing neural
network-based methods for this challenging problem. However, neural samplers
typically incur heavy computational overhead due to simulating trajectories
during training. This motivates the pursuit of simulation-free training
procedures of neural samplers. In this work, we propose an elegant modification
to previous methods, which allows simulation-free training with the help of a
time-dependent normalizing flow. However, it ultimately suffers from severe
mode collapse. On closer inspection, we find that nearly all successful neural
samplers rely on Langevin preconditioning to avoid mode collapsing. We
systematically analyze several popular methods with various objective functions
and demonstrate that, in the absence of Langevin preconditioning, most of them
fail to adequately cover even a simple target. Finally, we draw attention to a
strong baseline by combining the state-of-the-art MCMC method, Parallel
Tempering (PT), with an additional generative model to shed light on future
explorations of neural samplers.
|
2502.06689
|
Neumann eigenmaps for landmark embedding
|
math.ST cs.LG cs.NA math.NA stat.ML stat.TH
|
We present Neumann eigenmaps (NeuMaps), a novel approach for enhancing the
standard diffusion map embedding using landmarks, i.e distinguished samples
within the dataset. By interpreting these landmarks as a subgraph of the larger
data graph, NeuMaps are obtained via the eigendecomposition of a renormalized
Neumann Laplacian. We show that NeuMaps offer two key advantages: (1) they
provide a computationally efficient embedding that accurately recovers the
diffusion distance associated with the reflecting random walk on the subgraph,
and (2) they naturally incorporate the Nystr\"om extension within the diffusion
map framework through the discrete Neumann boundary condition. Through examples
in digit classification and molecular dynamics, we demonstrate that NeuMaps not
only improve upon existing landmark-based embedding methods but also enhance
the stability of diffusion map embeddings to the removal of highly significant
points.
|
2502.06692
|
Multi-label Scandinavian Language Identification (SLIDE)
|
cs.CL cs.AI
|
Identifying closely related languages at sentence level is difficult, in
particular because it is often impossible to assign a sentence to a single
language. In this paper, we focus on multi-label sentence-level Scandinavian
language identification (LID) for Danish, Norwegian Bokm\r{a}l, Norwegian
Nynorsk, and Swedish. We present the Scandinavian Language Identification and
Evaluation, SLIDE, a manually curated multi-label evaluation dataset and a
suite of LID models with varying speed-accuracy tradeoffs. We demonstrate that
the ability to identify multiple languages simultaneously is necessary for any
accurate LID method, and present a novel approach to training such multi-label
LID models.
|
2502.06693
|
Recent Advances, Applications and Open Challenges in Machine Learning
for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
|
cs.LG cs.AI cs.CY
|
The fourth Machine Learning for Health (ML4H) symposium was held in person on
December 15th and 16th, 2024, in the traditional, ancestral, and unceded
territories of the Musqueam, Squamish, and Tsleil-Waututh Nations in Vancouver,
British Columbia, Canada. The symposium included research roundtable sessions
to foster discussions between participants and senior researchers on timely and
relevant topics for the ML4H community. The organization of the research
roundtables at the conference involved 13 senior and 27 junior chairs across 13
tables. Each roundtable session included an invited senior chair (with
substantial experience in the field), junior chairs (responsible for
facilitating the discussion), and attendees from diverse backgrounds with an
interest in the session's topic.
|
2502.06695
|
FairDropout: Using Example-Tied Dropout to Enhance Generalization of
Minority Groups
|
cs.LG
|
Deep learning models frequently exploit spurious features in training data to
achieve low training error, often resulting in poor generalization when faced
with shifted testing distributions. To address this issue, various methods from
imbalanced learning, representation learning, and classifier recalibration have
been proposed to enhance the robustness of deep neural networks against
spurious correlations. In this paper, we observe that models trained with
empirical risk minimization tend to generalize well for examples from the
majority groups while memorizing instances from minority groups. Building on
recent findings that show memorization can be localized to a limited number of
neurons, we apply example-tied dropout as a method we term FairDropout, aimed
at redirecting this memorization to specific neurons that we subsequently drop
out during inference. We empirically evaluate FairDropout using the
subpopulation benchmark suite encompassing vision, language, and healthcare
tasks, demonstrating that it significantly reduces reliance on spurious
correlations, and outperforms state-of-the-art methods.
|
2502.06698
|
Heisenberg-limited calibration of entangling gates with robust phase
estimation
|
quant-ph cs.SY eess.SY
|
The calibration of high-quality two-qubit entangling gates is an essential
component in engineering large-scale, fault-tolerant quantum computers.
However, many standard calibration techniques are based on randomized circuits
that are only quadratically sensitive to calibration errors. As a result, these
approaches are inefficient, requiring many experimental shots to achieve
acceptable performance. In this work, we demonstrate that robust phase
estimation can enable high-precision, Heisenberg-limited estimates of coherent
errors in multi-qubit gates. Equipped with an efficient estimator, the
calibration problem may be reduced to a simple optimization loop that minimizes
the estimated coherent error. We experimentally demonstrate our calibration
protocols by improving the operation of a two-qubit controlled-Z gate on a
superconducting processor, and we validate the improved performance with gate
set tomography. Our methods are applicable to gates in other quantum hardware
platforms such as ion traps and neutral atoms, and on other multi-qubit gates,
such as CNOT or iSWAP.
|
2502.06703
|
Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time
Scaling
|
cs.CL
|
Test-Time Scaling (TTS) is an important method for improving the performance
of Large Language Models (LLMs) by using additional computation during the
inference phase. However, current studies do not systematically analyze how
policy models, Process Reward Models (PRMs), and problem difficulty influence
TTS. This lack of analysis limits the understanding and practical use of TTS
methods. In this paper, we focus on two core questions: (1) What is the optimal
approach to scale test-time computation across different policy models, PRMs,
and problem difficulty levels? (2) To what extent can extended computation
improve the performance of LLMs on complex tasks, and can smaller language
models outperform larger ones through this approach? Through comprehensive
experiments on MATH-500 and challenging AIME24 tasks, we have the following
observations: (1) The compute-optimal TTS strategy is highly dependent on the
choice of policy model, PRM, and problem difficulty. (2) With our
compute-optimal TTS strategy, extremely small policy models can outperform
larger models. For example, a 1B LLM can exceed a 405B LLM on MATH-500.
Moreover, on both MATH-500 and AIME24, a 0.5B LLM outperforms GPT-4o, a 3B LLM
surpasses a 405B LLM, and a 7B LLM beats o1 and DeepSeek-R1, while with higher
inference efficiency. These findings show the significance of adapting TTS
strategies to the specific characteristics of each task and model and indicate
that TTS is a promising approach for enhancing the reasoning abilities of LLMs.
|
2502.06705
|
RSAttAE: An Information-Aware Attention-based Autoencoder Recommender
System
|
cs.LG cs.IR
|
Recommender systems play a crucial role in modern life, including information
retrieval, the pharmaceutical industry, retail, and entertainment. The
entertainment sector, in particular, attracts significant attention and
generates substantial profits. This work proposes a new method for predicting
unknown user-movie ratings to enhance customer satisfaction. To achieve this,
we utilize the MovieLens 100K dataset. Our approach introduces an
attention-based autoencoder to create meaningful representations and the
XGBoost method for rating predictions. The results demonstrate that our
proposal outperforms most of the existing state-of-the-art methods.
Availability: github.com/ComputationIASBS/RecommSys
|
2502.06707
|
FinMamba: Market-Aware Graph Enhanced Multi-Level Mamba for Stock
Movement Prediction
|
cs.CE
|
Recently, combining stock features with inter-stock correlations has become a
common and effective approach for stock movement prediction. However, financial
data presents significant challenges due to its low signal-to-noise ratio and
the dynamic complexity of the market, which give rise to two key limitations in
existing methods. First, the relationships between stocks are highly influenced
by multifaceted factors including macroeconomic market dynamics, and current
models fail to adaptively capture these evolving interactions under specific
market conditions. Second, for the accuracy and timeliness required by
real-world trading, existing financial data mining methods struggle to extract
beneficial pattern-oriented dependencies from long historical data while
maintaining high efficiency and low memory consumption. To address the
limitations, we propose FinMamba, a Mamba-GNN-based framework for market-aware
and multi-level hybrid stock movement prediction. Specifically, we devise a
dynamic graph to learn the changing representations of inter-stock
relationships by integrating a pruning module that adapts to market trends.
Afterward, with a selective mechanism, the multi-level Mamba discards
irrelevant information and resets states to skillfully recall historical
patterns across multiple time scales with linear time costs, which are then
jointly optimized for reliable prediction. Extensive experiments on U.S. and
Chinese stock markets demonstrate the effectiveness of our proposed FinMamba,
achieving state-of-the-art prediction accuracy and trading profitability, while
maintaining low computational complexity. The code is available at
https://github.com/TROUBADOUR000/FinMamba.
|
2502.06708
|
TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic
Videos using Surgical Timeline Segmentation
|
cs.CV
|
Indexing endoscopic surgical videos is vital in surgical data science,
forming the basis for systematic retrospective analysis and clinical
performance evaluation. Despite its significance, current video analytics rely
on manual indexing, a time-consuming process. Advances in computer vision,
particularly deep learning, offer automation potential, yet progress is limited
by the lack of publicly available, densely annotated surgical datasets. To
address this, we present TEMSET-24K, an open-source dataset comprising 24,306
trans-anal endoscopic microsurgery (TEMS) video micro-clips. Each clip is
meticulously annotated by clinical experts using a novel hierarchical labeling
taxonomy encompassing phase, task, and action triplets, capturing intricate
surgical workflows. To validate this dataset, we benchmarked deep learning
models, including transformer-based architectures. Our in silico evaluation
demonstrates high accuracy (up to 0.99) and F1 scores (up to 0.99) for key
phases like Setup and Suturing. The STALNet model, tested with ConvNeXt, ViT,
and SWIN V2 encoders, consistently segmented well-represented phases.
TEMSET-24K provides a critical benchmark, propelling state-of-the-art solutions
in surgical data science.
|
2502.06710
|
Learning Musical Representations for Music Performance Question
Answering
|
cs.CV cs.MM cs.SD eess.AS
|
Music performances are representative scenarios for audio-visual modeling.
Unlike common scenarios with sparse audio, music performances continuously
involve dense audio signals throughout. While existing multimodal learning
methods on the audio-video QA demonstrate impressive capabilities in general
scenarios, they are incapable of dealing with fundamental problems within the
music performances: they underexplore the interaction between the multimodal
signals in performance and fail to consider the distinctive characteristics of
instruments and music. Therefore, existing methods tend to answer questions
regarding musical performances inaccurately. To bridge the above research gaps,
(i) given the intricate multimodal interconnectivity inherent to music data,
our primary backbone is designed to incorporate multimodal interactions within
the context of music; (ii) to enable the model to learn music characteristics,
we annotate and release rhythmic and music sources in the current music
datasets; (iii) for time-aware audio-visual modeling, we align the model's
music predictions with the temporal dimension. Our experiments show
state-of-the-art effects on the Music AVQA datasets. Our code is available at
https://github.com/xid32/Amuse.
|
2502.06715
|
HoneyComb: A Parallel Worst-Case Optimal Join on Multicores
|
cs.DB
|
To achieve true scalability on massive datasets, a modern query engine needs
to be able to take advantage of large, shared-memory, multicore systems. Binary
joins are conceptually easy to parallelize on a multicore system; however,
several applications require a different approach to query evaluation, using a
Worst-Case Optimal Join (WCOJ) algorithm. WCOJ is known to outperform
traditional query plans for cyclic queries. However, there is no obvious
adaptation of WCOJ to parallel architectures. The few existing systems that
parallelize WCOJ do this by partitioning only the top variable of the WCOJ
algorithm. This leads to work skew (since some relations end up being read
entirely by every thread), possible contention between threads (when the
hierarchical trie index is built lazily, which is the case on most recent WCOJ
systems), and exacerbates the redundant computations already existing in WCOJ.
We introduce HoneyComb, a parallel version of WCOJ, optimized for large
multicore, shared-memory systems. HoneyComb partitions the domains of all query
variables, not just that of the top loop. We adapt the partitioning idea from
the HyperCube algorithm, developed by the theory community for computing
multi-join queries on a massively parallel shared-nothing architecture, and
introduce new methods for computing the shares, optimized for a shared-memory
architecture. To avoid the contention created by the lazy construction of the
trie-index, we introduce CoCo, a new and very simple index structure, which we
build eagerly, by sorting the entire relation. Finally, in order to remove some
of the redundant computations of WCOJ, we introduce a rewriting technique of
the WCOJ plan that factors out some of these redundant computations. Our
experimental evaluation compares HoneyComb with several recent implementations
of WCOJ.
|
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