AI-generated summaries

Today's ML research,
without the noise.

Daily summaries of the latest machine learning papers from arXiv, processed every 8 hours.

48 Papers today
8h Update frequency
7 Days of history
Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
Taida Li, Yujun Yan, Fei Dou, Wenzhan Song, Xiang Zhang
Time Series
  • Inter-subject variability poses significant challenges for deep learning in EEG decoding.
  • The survey categorizes methodologies into distinct families addressing cross-subject generalization.
  • Rigorous evaluation protocols are essential for assessing the effectiveness of these methodologies.
  • The paper emphasizes the importance of utilizing subject identity and metadata in model training.
Read more
Open Problems in Frontier AI Risk Management
Marta Ziosi, Miro Plueckebaum, Stephen Casper, Henry Papadatos, Ze Shen Chin, Peter Slattery, James Gealy, Tim G. J. Rudner, Brian Tse, Ariel Gil, Patricia Paskov, Maximilian Negele, Rokas Gipiškis, Nada Madkour, Vera Lummis, Rupal Jain, Luise Eder, Kristina Fort, Malou C. van Draanen Glismann, Inès Belhadj, Amin Oueslati, Anna K. Wisakanto, Richard Mallah, Koen Holtman, Ranj Zuhdi, Daniel S. Schiff, Jessica Newman, Malcolm Murray, Robert Trager
Theory
  • Frontier AI systems introduce novel safety risks that existing risk management frameworks are ill-equipped to handle.
  • The paper identifies and categorizes open problems in the risk management process for frontier AI.
  • Different types of open problems require tailored responses from various stakeholders.
  • The authors provide a structured review of the literature to highlight unresolved challenges in risk management.
Read more
Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification
Eklavya Sarkar, Marius Miron, David Robinson, Gagan Narula, Milad Alizadeh, Ellen Gilsenan-McMahon, Emmanuel Chemla, Olivier Pietquin, Matthieu Geist
Audio & Speech
  • Existing bioacoustic systems are limited by pre-trained models that only utilize the 0-8 kHz baseband.
  • The proposed multi-band encoding framework effectively captures higher-frequency information from animal vocalizations.
  • Fused representations from multi-band encoding outperform traditional methods in classification tasks.
  • The study provides an open-source toolkit for the bioacoustics community to implement the proposed methods.
Read more
FiLMMeD: Feature-wise Linear Modulation for Cross-Problem Multi-Depot Vehicle Routing
Arthur Corrêa, Paulo Nascimento, Samuel Moniz
Optimization
  • FiLMMeD is the first MTL model explicitly targeting the MDVRP.
  • Introduces Feature-wise Linear Modulation to enhance generalization across diverse constraints.
  • Demonstrates Preference Optimization as a superior alternative to Reinforcement Learning in MTL.
  • Employs a targeted curriculum learning strategy to improve model training.
Read more
Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
Matthias Hertel, Alexandra Nikoltchovska, Sebastian Pütz, Ralf Mikut, Benjamin Schäfer, Veit Hagenmeyer
Time Series Interpretability
  • Introduction of an efficient SHAP-based explainability algorithm for TSFMs.
  • Evaluation of Chronos-2 and TabPFN-TS for load forecasting against state-of-the-art models.
  • Demonstration of meaningful use of covariates in load predictions.
  • Alignment of model explanations with established domain knowledge.
Read more
A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification
Ethan Harvey, Dennis Johan Loevlie, Amir Ali Satani, Wansu Chen, David M. Kent, Michael C. Hughes
Computer Vision Efficient ML
  • Mean pooling MIL outperforms or matches advanced MIL and 3D CNN methods on several tasks.
  • Attention-based methods do not significantly improve performance compared to simple mean pooling.
  • The study highlights the efficiency of mean pooling MIL, being 25 times faster to train than complex alternatives.
  • Quality of learned attention in existing MIL methods is questioned, with trivial baselines performing comparably.
Read more
AutoSP: Unlocking Long-Context LLM Training Via Compiler-Based Sequence Parallelism
Ahan Gupta, Zhihao Wang, Neel Dani, Masahiro Tanaka, Olatunji Ruwase, Minjia Zhang
Large Language Models NLP Efficient ML
  • AutoSP is the first automated solution for optimizing LLM training for long-context tasks.
  • It integrates sequence parallelism and activation-checkpointing into the PyTorch-2.0 compilation stack.
  • AutoSP significantly increases training context lengths without compromising runtime performance.
  • The solution simplifies the implementation of complex long-context optimizations for developers.
Read more
Random Cloud: Finding Minimal Neural Architectures Without Training
Javier Gil Blázquez
Efficient ML
  • Introduces a training-free method for neural architecture search called Random Cloud.
  • Achieves significant parameter reduction while maintaining or improving accuracy compared to traditional pruning methods.
  • Evaluates networks without backpropagation, reducing computational costs associated with full training cycles.
  • Demonstrates effectiveness across seven diverse classification datasets.
Read more
AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data
Ali Jaberi, Yonatan Kurniawan, Robert Black, Shayan Mousavi M., Kabir Verma, Zoya Sadighi, Santiago Miret, Jason Hattrick-Simpers
Reinforcement Learning
  • AutoREC is an open-source platform for automating ECM generation from EIS data using reinforcement learning.
  • The platform formulates ECM construction as a sequential decision-making problem, improving scalability and efficiency.
  • The RL agent achieved over 99.6% success on synthetic datasets and showed strong generalization to real-world EIS data.
  • AutoREC addresses the limitations of traditional manual ECM identification methods, enabling faster and more consistent analysis.
Read more
Mini-Batch Class Composition Bias in Link Prediction
Kieran Maguire, Srinandan Dasmahapatra
Graph Learning
  • GNNs trained for link prediction may learn trivial heuristics rather than meaningful representations.
  • Mini-batch class composition introduces bias that affects the learning of graph features.
  • Randomizing mini-batch composition improves feature alignment with node classification tasks.
  • Current link prediction methods may overestimate their ability to generalize across tasks.
Read more
NeuroPlastic: A Plasticity-Modulated Optimizer for Biologically Inspired Learning Dynamics
Douglas Jiang, Yuechen Wang, Jiayi Wang, Jiaying Geng, Qinglong Wang, Feng Tian
Optimization
  • NeuroPlastic introduces a biologically inspired optimization approach that combines multiple learning signals.
  • The optimizer shows significant improvements over traditional gradient-only methods, particularly in challenging datasets.
  • A stabilization mechanism is included to maintain stable optimization dynamics across various learning rates.
  • The framework provides reproducible benchmarks and diagnostic tools for analyzing optimization behavior.
Read more
Differentiable latent structure discovery for interpretable forecasting in clinical time series
Ivan Lerner, Jean Feydy, Alexandre Kalimouttou, Anita Burgun, Francis Bach
Time Series Interpretability Optimization
  • Introduction of StructGP for continuous-time multi-task Gaussian process modeling.
  • Differentiable structure learning enables the discovery of interpretable dependency structures.
  • LP-StructGP captures cross-patient progression patterns through latent pathways.
  • Models demonstrate superior forecasting accuracy and uncertainty calibration on clinical datasets.
Read more
A Randomized PDE Energy driven Iterative Framework for Efficient and Stable PDE Solutions
Yi Bing, Zheng Ran, Fu Jinyang, Liu Long, Peng Xiang
Theory Efficient ML
  • Introduces a PDE energy-driven framework that avoids classical matrix-based solvers and data-driven training.
  • Utilizes physically constrained diffusion iterations and Gaussian smoothing for evolving initial fields.
  • Demonstrates stable convergence and accurate resolution of sharp gradients in various PDEs.
  • Achieves competitive accuracy and stability compared to traditional numerical methods.
Read more
A Unified Framework of Hyperbolic Graph Representation Learning Methods
Sofía Pérez Casulo, Marcelo Fiori, Bernardo Marenco, Federico Larroca
Graph Learning
  • Introduction of HypeGRL, a unified framework for hyperbolic graph representation learning.
  • Facilitates reproducible research and systematic evaluation of hyperbolic embedding methods.
  • Experimental evaluation highlights performance differences in link prediction and node classification tasks.
  • Provides practical insights into the strengths and limitations of existing hyperbolic GRL approaches.
Read more
BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning
Yizhou Wu, Shansong Wang, Yuheng Li, Mojtaba Safari, Mingzhe Hu, Chih-Wei Chang, Harini Veeraraghavan, Xiaofeng Yang
Computer Vision Efficient ML
  • BrainDINO is a self-supervised model that generalizes across diverse brain MRI tasks.
  • It was trained on a large dataset of 6.6 million unlabeled axial slices from 20 different sources.
  • The model outperformed existing self-supervised learning baselines, especially under conditions of limited labeled data.
  • BrainDINO's representations are anatomically organized and pathology-sensitive, enhancing its clinical applicability.
Read more
Mind the Gap: Structure-Aware Consistency in Preference Learning
Mehryar Mohri, Yutao Zhong
NLP Large Language Models Theory
  • Standard surrogate minimization in preference learning can lead to vacuous consistency guarantees.
  • The authors introduce a margin-shifted ranking framework to enforce H-consistency in preference learning.
  • SA-DPO adapts margins based on semantic distances, improving handling of synonyms and ambiguous pairs.
  • The Margin-Capacity Profile quantifies the trade-off between theoretical consistency and model capacity.
Read more
A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms
Catherine Ning, Yu Ma, Cindy Beini Wang, Sean McMahon, Joseph Radojevic, Steven Zweibel, Dimitris Bertsimas
Multimodal
  • Developed a multimodal machine learning framework for LVEF assessment using ECG and EHR data.
  • Achieved high classification performance across four LVEF categories, outperforming unimodal models.
  • Utilized SHAP for feature attribution to enhance model explainability.
  • Demonstrated potential for practical application in resource-limited healthcare settings.
Read more
Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control
Bolian Li, Yifan Wang, Yi Ding, Anamika Lochab, Ananth Grama, Ruqi Zhang
Large Language Models Reinforcement Learning Theory
  • Introduces Entrocraft for precise entropy control in RL training of LLMs.
  • Theoretical insights connect entropy changes to advantage distributions.
  • Linear annealing of entropy schedules is found to be the most effective.
  • Empirical results show significant improvements in generalization and output diversity.
Read more
Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift
Haiyang Zhao
Reinforcement Learning Computer Vision Robotics
  • OOD detection alone is insufficient for effective adaptation in visual MBRL under dynamics shift.
  • JEPA-Indexed Local Expert Growth separates problem indexing from action correction, preserving baseline controller performance.
  • The proposed method demonstrates significant OOD improvements while maintaining in-distribution performance.
  • Learned experts can be reused for recurring shifts, supporting incremental knowledge growth.
Read more
Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition
Georgios I. Orfanidis, Dimitris A. Pados, George Sklivanitis, Elizabeth Serena Bentley
Robotics Optimization Theory
  • Introduction of a Hankel-structured sensing framework for DoA estimation.
  • L2-norm estimator achieves maximum-likelihood optimality in Gaussian noise.
  • L1-norm estimator shows robustness in Laplace noise, suitable for real-world applications.
  • Extensive simulations demonstrate significant improvements in super-resolution capabilities.
Read more
Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
Mehryar Mohri, Yutao Zhong
Theory Optimization Efficient ML
  • Introduction of Linear-Core Surrogates that combine smoothness with linear consistency rates.
  • Proven differentiability and strict linear H-consistency bounds for the proposed loss functions.
  • Significant computational advantages in structured prediction, allowing for unbiased stochastic gradient estimation.
  • Empirical results show a 23× speedup over Structured SVMs and improved robustness to label noise.
Read more
Strait: Perceiving Priority and Interference in ML Inference Serving
Haidong Zhao, Nikolaos Georgantas
Efficient ML Optimization Theory
  • Strait enhances deadline satisfaction for dual-priority inference traffic under high GPU utilization.
  • The system models data transfer and kernel execution interference to improve latency estimation.
  • Priority-aware scheduling is implemented to differentiate handling of high and low-priority tasks.
  • Strait reduces deadline violations for high-priority tasks significantly while keeping low-priority task performance acceptable.
Read more
Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes
Minghao Gu, Weizhi Lin, Qiang Huang
Theory
  • Introduces a covariate-driven approach to model spatial deformation in nonstationary Gaussian processes.
  • Establishes a connection between diffeomorphic deformations and covariate vectors using velocity fields in a Lie algebra.
  • Develops an efficient estimation-inference algorithm for out-of-sample predictions.
  • Demonstrates the method's effectiveness through simulation and case studies in manufacturing and geostatistics.
Read more
Momentum-Conserving Graph Neural Networks for Deformable Objects
Jiahong Wang, Logan Numerow, Stelian Coros, Christian Theobalt, Vahid Babaei, Bernhard Thomaszewski
Graph Learning Robotics
  • Introduction of MomentumGNN, a GNN architecture that conserves momentum.
  • Utilization of per-edge impulses to ensure accurate momentum tracking.
  • Layer-by-layer architecture for sequential updates of vertex positions.
  • Unsupervised training using a physics-based loss function.
Read more
STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices
Yongliang Ding, Qigong Bi, Peng Pu
Graph Learning Time Series Optimization
  • STLGT improves tail latency prediction accuracy by 8.5% MAPE compared to PERT-GNN.
  • Achieves up to 12× faster CPU inference at scale, enhancing efficiency.
  • Utilizes a linear graph transformer to model cross-service dependencies effectively.
  • Incorporates a decoupled temporal module for better handling of workload dynamics.
Read more
Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts
Taylor Maxson, Roberto Corizzo, Yaning Wu, Nathalie Japkowicz, Colin Bellinger
Theory Multimodal
  • Unweighted evaluation metrics can mislead performance assessments in imbalanced classification due to within-class heterogeneity.
  • The proposed predicted-weighted balanced accuracy (pBA) metric utilizes predicted posterior probabilities to provide a more accurate evaluation.
  • Empirical results show that pBA outperforms traditional metrics in scenarios with uneven subconcept distributions.
  • The study emphasizes the importance of considering minority subconcept performance for reliable model deployment.
Read more
Efficient and Interpretable Transformer for Counterfactual Fairness
Panyi Dong, Zhiyu Quan
Efficient ML Interpretability Theory
  • Introduction of FCorrTransformer, an efficient and interpretable model for tabular data.
  • Development of Counterfactual Attention Regularization (CAR) to enforce fairness without causal assumptions.
  • Empirical results show strong performance in counterfactual fairness and predictive accuracy.
  • The approach addresses bias at a structural level rather than through feature exclusion.
Read more
Privacy-Preserving Federated Learning Framework for Distributed Chemical Process Optimization
Teetat Pipattaratonchai, Aueaphum Aueawatthanaphisut
Federated Learning Optimization Time Series
  • Application of federated learning to distributed chemical process systems.
  • Development of a cross-plant learning framework for heterogeneous environments.
  • Incorporation of secure parameter aggregation mechanisms for data protection.
  • Demonstration of improved prediction accuracy through experimental evaluations.
Read more
Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning
Seungyub Han, Hyungjin Kim, Jungwoo Lee
Reinforcement Learning Robotics Theory
  • Introduction of Self-Alignment for Safety (SAS) for test-time adaptation in offline safe RL.
  • Utilization of Lyapunov stability as an occupancy-measure criterion for ensuring safety.
  • Hierarchical RL interpretation through Bayesian inference over latent skills.
  • Empirical results show SAS outperforms existing safe RL methods, reducing costs and failures.
Read more
Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
Jinjiang Guo
Graph Learning
  • Classical machine learning models outperform larger models in many tasks related to molecular property prediction.
  • Graph neural networks perform well but do not consistently surpass compact models.
  • Pretrained molecular sequence models show limited effectiveness in this context.
  • Performance is highly dependent on the specific task and data characteristics.
Read more
SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations
Jason Wu, Shir-Kang Scott Jinn, Yuyang Yuan, Maggie Wigness, Lance M. Kaplan, Hang Qiu, Mani Srivastava
Multimodal Robotics Efficient ML
  • SWAN is the first multimodal network that adapts resource allocation based on modality quality, sample complexity, and a user-defined budget.
  • The QoI-aware controller optimally selects layer configurations while maintaining end-to-end differentiability using NeuralSort.
  • The SkipGate module conditionally executes layers based on input features, enhancing efficiency.
  • SWAN achieves significant reductions in computational load while maintaining high detection performance in autonomous driving tasks.
Read more
CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs
Zhe Ding, Su Pan, Duowei Pan
NLP Large Language Models Efficient ML
  • CoQuant introduces a joint weight-activation subspace projection method for mixed-precision quantization.
  • The method balances activation and weight covariances to optimize the selection of high-precision subspaces.
  • Extensive experiments show CoQuant outperforms strong PTQ baselines in perplexity and reasoning tasks.
  • The approach addresses the limitations of existing methods that rely solely on activation statistics.
Read more
Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework
Zhenzhou Jin, Li You, Xiang-Gen Xia, Xiqi Gao
Optimization Theory Efficient ML
  • Introduction of a unified tensor learning framework for constructing statistical channel fingerprints (sCF).
  • Establishment of a relationship between channel spatial covariance matrix (CSCM) and channel power angular spectrum (CPAS).
  • Development of LPWTNet architecture that utilizes Laplacian pyramid decomposition for efficient inference.
  • Implementation of a shared mask learning strategy for adaptive refinement of high-frequency components.
Read more
Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry
Wenhao Lan, Shan Li, Junbin Yang, Haihua Shen, Yijun Yang
NLP Large Language Models
  • Introduces a trajectory-level measurement protocol for analyzing refusal geometry in language models.
  • Demonstrates that R2D2 fine-tuning traces a robustness-utility frontier, showing that robust refusal cannot be solely evaluated by attack success.
  • Provides evidence for the reorganization of refusal carriers rather than simple drift, with significant implications for model training.
  • Causal interventions reveal that control over refusal is low-dimensional but closely linked to utility, challenging previous assumptions about independent refusal pathways.
Read more
PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners
Zhiquan Tan, Yinrong Hong
NLP Large Language Models Reinforcement Learning
  • PAINT improves reasoning in LLMs by adapting the exposure of solution context based on rollout-reference overlap.
  • The method employs sparse teacher energy interpolation to target specific token positions for better supervision.
  • Empirical results show consistent gains over prior self-distillation methods and competitive performance against GRPO.
  • PAINT achieves better rollout-token efficiency with shorter training rollouts compared to traditional methods.
Read more
Optimized Deferral for Imbalanced Settings
Corinna Cortes, Anqi Mao, Mehryar Mohri, Yutao Zhong
NLP Large Language Models Efficient ML
  • Introduces a novel cost-sensitive learning framework for deferral in imbalanced expert settings.
  • Develops new margin-based loss functions and algorithms specifically for expert imbalance.
  • Presents the MILD algorithm, which shows significant performance improvements in practical tasks.
  • Demonstrates the effectiveness of the proposed methods through extensive empirical evaluations.
Read more
Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture
Samuel L Pugh, Eric Yang, Alexander Muir Sutherland, Alessandra Breschi
NLP Large Language Models Generative Models
  • The Dual-Stream Memory Architecture effectively separates patient narratives from clinical records, enhancing data integrity.
  • The Reconciliation Engine actively flags discrepancies, allowing for timely clinical evaluations rather than silent updates.
  • The study quantifies a 13.6% error cascade, identifying upstream memory extraction errors as critical bottlenecks in clinical AI pipelines.
  • Continuous validation of patient statements against clinical records is essential for safe healthcare applications of AI.
Read more
Low Rank Adaptation for Adversarial Perturbation
Han Liu, Shanghao Shi, Yevgeniy Vorobeychik, Chongjie Zhang, Ning Zhang
Theory Optimization Efficient ML
  • Adversarial perturbations possess an inherently low-rank structure.
  • The proposed method improves the efficiency and effectiveness of black-box adversarial attacks.
  • The approach utilizes auxiliary data and a reference model to construct a low-rank subspace.
  • Integrating low-rank optimization can reduce computational overhead in adversarial training.
Read more
Anomaly Detection in Soil Heavy Metal Contamination Using Unsupervised Learning for Environmental Risk Assessment
Isaac Tettey Adjokatse, Samuel Senyo Koranteng, George Yamoah Afrifa, Theophilus Ansah-Narh, Marcellin Atemkeng, Joseph Bremang Tandoh, Kow Ahor Essel-Yorke, Richmond Opoku-Sarkodie, Rebecca Davis
Theory
  • Unsupervised machine learning effectively detects soil heavy metal contamination anomalies.
  • Isolation Forest and PCA reconstruction error identified significant anomalies in soil samples.
  • Anomalies exhibited 70-80% higher Hazard Index values compared to normal samples.
  • Three distinct types of contamination anomalies were identified at specific sites.
Read more
On the Expressive Power of GNNs to Solve Linear SDPs
Chendi Qian, Christopher Morris
Graph Learning Optimization Theory
  • Standard GNN architectures fail to recover solutions for linear SDPs.
  • The VC-2-FWL architecture is proposed as a more expressive alternative capable of capturing the structure of SDPs.
  • Empirical results show that VC-2-FWL achieves lower prediction errors and objective gaps compared to weaker models.
  • Utilizing predictions from VC-2-FWL to warm-start traditional solvers can lead to significant computational speedups.
Read more
Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
Abhirami Pillai
Optimization Reinforcement Learning Theory
  • BCCB is an online framework that combines HTE estimation, exploration, and budget pacing into a unified decision-making process.
  • It operates effectively without the need for historical data, making it suitable for cold-start scenarios.
  • BCCB shows 3-5x lower performance variance than traditional offline methods, enhancing predictability in campaign planning.
  • The framework consistently outperforms existing online methods, particularly at higher budget levels.
Read more
Generalizing the Geometry of Model Merging Through Fréchet Averages
Marvin F. da Silva, Mohammed Adnan, Felix Dangel, Sageev Oore
Theory Optimization Efficient ML
  • Model merging can be fragile due to architectural symmetries; traditional averaging methods may fail.
  • Fréchet averaging provides a symmetry-aware merging solution by minimizing geodesic distances on a manifold.
  • The choice of geometry (metric and manifold) is critical for effective model merging.
  • The paper introduces a practical algorithm for merging low-rank adapters (LoRA) that addresses alignment issues.
Read more
Hierarchical adaptive control for real-time dynamic inference at the edge
Francesco Daghero, Mahyar Tourchi Moghaddam, Mikkel Baun Kjærgaard
Efficient ML
  • Introduction of a budgeted specialized predictor cascade that adheres to worst-case latency constraints.
  • Development of a hierarchical control system that adapts to data and resource changes in real-time.
  • Demonstration of substantial reductions in latency (up to 2.45x) and energy consumption (up to 2.86x) with minimal accuracy drop (<4%) compared to static baselines.
  • Focus on enhancing the deployment of dynamic ML models in edge computing environments.
Read more
PPG-Based Affect Recognition with Long-Range Deep Models: A Measurement-Driven Comparison of CNN, Transformer, and Mamba Architectures
Karim Alghoul, Hussein Al Osman, Abdulmotaleb El Saddik
Time Series
  • Comparison of CNN, CNN-LSTM, Transformer, and Mamba architectures for PPG-based affect recognition.
  • Transformers and Mamba models show comparable performance to CNNs but do not consistently outperform them.
  • CNNs achieve the highest accuracy and efficiency, making them the most effective overall.
  • Transformers provide a better balance of F1 scores for arousal and relaxation states.
Read more
reward-lens: A Mechanistic Interpretability Library for Reward Models
Mohammed Suhail B Nadaf
NLP Large Language Models Reinforcement Learning
  • Introduction of 'reward-lens', the first toolkit for mechanistic interpretability of reward models.
  • The library organizes interpretability tools around the weight vector of the reward head.
  • Includes five theory-grounded extensions to enhance interpretability.
  • Empirical validation shows linear attribution does not predict causal importance.
Read more
PROMISE-AD: Progression-aware Multi-horizon Survival Estimation for Alzheimer's Disease Progression and Dynamic Tracking
Qing Lyu, Jeremy Hudson, Mohammad Kawas, Yuming Jiang, Chenyu You, Christopher T Whitlow
Time Series
  • Introduction of PROMISE-AD, a leakage-safe survival framework for AD progression prediction.
  • Development of progression-aware visit tokenization to handle irregular clinical histories.
  • Utilization of a temporal Transformer for effective risk estimation.
  • Achieved state-of-the-art performance in predicting AD conversion with low Brier scores and high C-index.
Read more
Diagnosing Capability Gaps in Fine-Tuning Data
Saeid Asgari Taghanaki, Rakshanda Agarwal, Bruce Sun, Rohan Jha, Elias Stengel-Eskin, Sara Malvar, Rui Ying, Yifei Xu, Guilherme Potje, Tusher Chakraborty, Leonardo de Oliveira Nunes, Ranveer Chandra, Emre Kiciman
NLP Large Language Models Reinforcement Learning
  • GOALCOVER enables systematic detection of capability gaps in fine-tuning datasets.
  • The framework decomposes high-level goals into atomic subgoals for better evaluation.
  • Controlled experiments validate GOALCOVER's effectiveness in identifying targeted capability impacts.
  • Training on GOALCOVER-filtered data improves model performance in downstream tasks.
Read more
Who Trains Matters: Federated Learning under Enrollment and Participation Selection Biases
Gota Morishita
Federated Learning
  • Introduces a two-stage selection framework for Federated Learning, addressing both enrollment and participation biases.
  • Develops FEDIPW, an inverse-probability-weighted aggregation method to recover the target-population mean update.
  • Proposes a limited-information aggregate-calibration extension for scenarios where client-level data is unavailable.
  • Demonstrates through experiments that enrollment correction effectively reduces target-population error.
Read more