PAIR-Former: Budgeted Relational MIL for miRNA Target Prediction
PAIR-Former applies budgeted relational multi-instance learning to miRNA target prediction with compute constraints and instance-level relational processing.
PAIR-Former applies budgeted relational multi-instance learning to miRNA target prediction with compute constraints and instance-level relational processing.
Addresses machine unlearning for sparse LLMs to remove memorized sensitive information while maintaining model sparsification benefits for efficient deployment.
ECHO-2 is distributed RL framework for LLM post-training via reinforcement learning, optimizing cost-efficiency of rollout generation across distributed resources.
VJE introduces reconstruction-free latent-variable framework for self-supervised learning using symmetric conditional ELBO on paired embeddings.
LP-FNO uses Fourier Neural Operators as surrogate model for laser welding simulations, enabling faster parametric solution learning for industrial process optimization.
Proposes radVI algorithm for variational inference by optimizing radial profiles to better approximate high-dimensional distributions beyond standard Gaussian surrogates.
DGPO: RL-guided graph diffusion model for neural architecture search using reinforcement learning steering.
Study using finetuned LLMs for topic-conditional sentiment extraction to forecast aluminum commodity prices.
Survey of privacy-preserving ML techniques for IoT including federated learning and differential privacy approaches.
SafeDriver-IQ: Framework using inverse crash probability modeling for real-time driver safety scoring.
Decentralized bi-level RL algorithm for environment design with sample-efficient hypergradient estimation.
MDM-Prime-v2: Improvements to masked diffusion language models through binary encoding and index shuffling.
Multi-view learning framework handling dimensional disparities across different feature views.
Federated learning approach using representation geometry to handle noisy annotations in distributed scenarios.
FIPO: RL algorithm improving token-level credit assignment for reasoning in LLMs beyond outcome-based rewards.
Safety-aware offline RL method using budget-conditioned reachability analysis for constrained decision-making.
SkillRouter: System for routing LLM agent requests to relevant skills from large skill libraries at inference time.
GNN layer using cost-sensitive neighborhood aggregation for heterophilous graph classification.
Theoretical analysis of online convex optimization with two-point bandit feedback and high-probability regret bounds.
GNN architecture addressing oversquashing through cross-attentive cohesive subgraph embedding.
ITQ3_S: 3-bit LLM quantization method using interleaved ternary quantization and rotation-domain smoothing for efficient inference.
Interpretability method for reinforcement learning using principal prototype analysis on manifolds.
Federated learning approach for livestock growth prediction addressing privacy and data scarcity in farm management.
Framework unifying gradient descent and Newton-type methods through quadratic gradient with synthesized Hessians.
Methods for uncertainty quantification in stochastic gradient descent using cheap resampling-based confidence intervals.
Neural network approach for prime number classification using sparse encoding.
Metrics for measuring predictability of recommender systems via structural complexity analysis.
Vision transformer optimization for image segmentation with adaptive computation per input image.
LLM-driven conversational recommender system for leisure event discovery with user-centric evaluation in SME context.
Image segmentation approach using divisive normalization for autonomous driving under diverse environmental conditions.
Framework for tightening convex relaxations of trained neural networks with convex and S-shaped activations for optimization incorporation.
Real-time operator takeover paradigm allowing seamless human intervention and correction during visuomotor diffusion policy execution.
Neural network method combining Gaussian processes with pre-trained priors to accelerate spatiotemporal inference on large datasets.
German-language LLM pre-training dataset curated via heuristic filtering, model-based selection, and synthetic data generation.
Meta-learning framework using LLMs to automatically design selection operators for evolutionary symbolic regression algorithms.
AVA-Bench systematically evaluates atomic visual abilities of vision foundation models independent of LLM instruction tuning.
SlowFast Sampling optimizes inference efficiency in diffusion-based language models through dynamic, flexible token generation strategies.
Streaming transformer architecture inspired by autoregressive LLMs for real-time 3D geometry perception and reconstruction from video.
Statistical methods for constructing confidence intervals for optimal treatment policy values using softmax smoothing in causal inference.
NES is an instruction-free code editing framework that learns from historical editing trajectories to suggest next edits with low latency.
Test-time adaptation method using domain augmentation and model ensembles to handle weather-related domain shifts in autonomous driving.
Knowledge distillation and self-supervised learning approach for continual learning with class-incremental learning and external unlabeled data.
Interpretability framework for understanding how components of particle swarm optimization algorithms affect performance.
Benchmark evaluating how large vision-language models handle object recognition in contextually incongruent scenes and manage uncertainty.
Wireless sensor network localization using distributed optimization algorithms for cooperative and non-cooperative positioning.
ProxyAttn method using representative attention heads to enable efficient sparse attention in LLMs for long-text processing with minimal performance degradation.
Multi-Stream Generative Policy framework for robot learning that combines multiple object-centric policies at inference to improve sample efficiency and generalization.
Algorithm for smooth quasar-convex optimization with general convex constraints achieving nearly optimal first-order query complexity.
Representation alignment technique for multimodal medical object detection addressing heterogeneous statistics across imaging modalities.
Transfer learning approach for assessing face image recognizability in unconstrained conditions without relying on visual heuristics.