SA-CycleGAN-2.5D: Self-Attention CycleGAN with Tri-Planar Context for Multi-Site MRI Harmonization
Self-attention CycleGAN variant for harmonizing MRI data across scanner sites to reduce acquisition variance.
Self-attention CycleGAN variant for harmonizing MRI data across scanner sites to reduce acquisition variance.
Approach for developing Tharu language LLM using synthetic data generation and human validation to address low-resource language gap.
Analysis of multimodal LLM segmentation capabilities through layerwise probing and attention mechanisms.
Gaussian process regression method handling input measurement uncertainty using Wasserstein distance.
Red-teaming alignment framework (CRAFT) that improves LLM robustness against jailbreaks by optimizing hidden representations.
Multi-agent RL framework for dynamic memory controller optimization with explainable energy and latency objectives.
Offline RL framework (PIER) for fuel-efficient maritime routing using physics-informed models and historical vessel data.
Multimodal LLM framework for ride-hailing dispute resolution combining visual and logical reasoning with transparency.
Neural network model for rapid prediction of free energies in polymer systems.
AI coding agent that bootstraps itself by re-implementing its own specification, demonstrating meta-circular properties similar to compiler bootstrapping.
Zero-shot learning approach using causal semantic distillation to transfer knowledge from seen to unseen classes.
Probabilistic inference method for high-dimensional image registration using variational approaches.
Data-driven model order reduction method for piecewise-linear nonlinear systems using dynamic mode decomposition.
Hardware-aware lossless compression technique (ZipServ) for efficient LLM inference with reduced memory and bandwidth requirements.
Research on conditional attention mechanism (L2A) that reduces computational costs for long-context LLM inference by selectively attending to relevant tokens.
arXiv paper proposing Riemannian Mirror Descent, generalizing first-order optimization methods to Riemannian manifolds with convergence guarantees.
Unified LLM for search, recommendation, and reasoning over large heterogeneous catalogs generating unambiguous item references under latency constraints.
Studies semi-factual explanations in XAI showing elaborated counterfactuals are preferred by users for understanding ML predictions and exploring alternatives.
Semantic ID-based generative retrieval system deployed at Spotify balancing long-term preferences with intent-aware podcast discovery using contextual signals.
Per-domain Q-value functions using graph neural networks for efficient policy learning in planning, cheaper than state-value alternatives.
Theoretical consistency analysis of k-nearest neighbor regressor under complex survey designs with derived convergence rate bounds.
Emergent Trust Learning: lightweight trust-based control algorithm for AI agents enabling cooperation in competitive multi-agent environments with shared resources.
Theoretical analysis proving graph transformers have structural benefits over GCNs for node-level prediction via Gaussian process limits.
HeiSD: Hybrid speculative decoding for Vision-Language-Action robot control models combining drafter-based and retrieval-based acceleration with kinematic awareness.
rSDNet: Robust neural learning framework defending against both label noise and adversarial attacks using alternative loss functions to standard cross-entropy.
State space models for biomolecular dynamics modeling, accelerating MD simulations while preserving temporal relationships for drug discovery applications.
Inhibitory normalization in neural circuits improves learning when handling complex input distributions, bridging biological and artificial neural networks.
Adaptive guidance mechanism for RAG-enhanced masked diffusion models to handle retrieval-prior conflicts when context is noisy or inconsistent.
Sensi: LLM agent architecture for ARC-AGI-3 using curriculum-based test-time learning with perception-action separation and active hypothesis testing.
Stochastic set-valued optimization framework for robust machine learning using multi-objective optimization with hyperbox set representations.
Uses shuffle products and finite-state automata to model overlapped speech for alignment and speaker-attributed transcription via marginalizing serializations.
CoVerRL framework escapes consensus trap in label-free LLM reasoning by using generator-verifier co-evolution to maintain output diversity and avoid reinforced systematic errors.
ResNet-50 pipeline for gastrointestinal video analysis using class reweighting and temporal decoding to handle severe class imbalance in pathology detection.
Framework integrating HPC, ML, and quantum computing for drug discovery, replacing trial-and-error with quantitative precision in molecular dynamics.
ChopGrad reduces memory costs in video diffusion model training by using truncated backpropagation with pixel-wise losses instead of accumulating activations.
Language model infers stage-play layouts (scenes, positions, movements) from narrative text without explicit spatial cues, testing spatial reasoning capabilities.
Text-to-Stage: LLM-based spatial reasoning from narratives to generate stage-play layouts. Probes compositional reasoning in language models.
Physics-informed ML surrogates for power grid simulation validation. Application of ML to scientific computing.
Research on how LLMs generate verbal confidence scores. Investigates timing and computation of uncertainty estimates in black-box models.
scicode-lint: LLM-generated patterns for detecting methodology bugs in scientific Python code. Addresses sustainability of ML-specific linters.
LoST: level-of-semantics tokenization method for 3D shape generation improving autoregressive 3D generative models.
Token pruning framework for efficient video vision-language models reducing computational cost via temporal token scoring.
Graph neural network method for air quality forecasting modeling pollution diffusion between cities and monitoring stations.
Aergia: federated learning system leveraging client heterogeneity in computing power to reduce training time.
Feature space renormalization mechanism for semi-supervised learning improving consistency regularization on unlabeled data.
Soft Dice Confidence: confidence estimator for selective prediction in semantic segmentation enabling model abstention.
Hi-GMAE: hierarchical graph masked autoencoders for multi-scale self-supervised learning on graph-structured data.
Analyzes transformer-based amortized causal discovery on observational data, bridging supervised learning with identifiability theory.
Den-TP: data curation framework addressing long-tail distribution in trajectory prediction datasets for autonomous driving.
ACT-JEPA: joint-embedding predictive architecture for efficient policy representation learning via self-supervised learning from unlabeled data.