6853 papers
This paper introduces Reverse Distillation, a framework that decomposes large protein language model representations into nested, orthogonal subspaces guided by smaller models to ensure consistent performance scaling and outperform existing baselines on ProteinGym benchmarks.
This paper proposes a lightweight, fully online Model Predictive Control framework for brand auction ads that utilizes isotonic regression to build monotonic bid models from streaming data, thereby improving spend efficiency and cost control without the need for complex machine learning models.
The paper proposes FedShift, a novel two-stage "Hide and Find" distributed adversarial attack for Federated Graph Learning that injects hidden shifters to stealthily guide poisoned data toward a target boundary and efficiently generates perturbations, achieving superior effectiveness, robustness against defenses, and a 90% reduction in convergence time compared to existing methods.
The paper proposes Uncertainty-Gated Generative Modeling (UGGM), a framework that utilizes uncertainty as an internal control signal to gate representation, propagation, and generation processes, achieving significant improvements in risk-sensitive financial time-series forecasting and robustness against regime shifts.
This paper introduces GANRA, a novel SMT solver that combines Large Language Models with GPU acceleration to efficiently solve quantifier-free nonlinear real arithmetic problems, achieving significant performance improvements over existing state-of-the-art methods.
This paper introduces MicroCoder-GRPO, an enhanced Group Relative Policy Optimization framework featuring innovations like conditional truncation masking and diversity-driven temperature selection, alongside a challenging new dataset and robust evaluator, to overcome training bottlenecks in modern coding models and achieve significant performance gains on LiveCodeBench v6.
This paper introduces a Lindbladian learning method that combines maximum-likelihood estimation on transient Pauli measurements with a neural differential equation framework to robustly infer open-system quantum dynamics, including dissipative mechanisms, across various hardware platforms and noise conditions with high efficiency.
This paper introduces MicroCoder, a high-quality dataset of curated, recent, and challenging competitive programming problems processed through a four-stage framework with automatic difficulty filtering, which significantly boosts coding model performance on unseen hard tasks compared to existing baselines.
ProgAgent is a JAX-native continual reinforcement learning agent that mitigates catastrophic forgetting and reward specification costs by unifying progress-aware reward learning from unlabeled videos with adversarial refinement and a stability-plasticity balanced architecture, achieving superior performance on robotic manipulation benchmarks.
This paper investigates the loss of plasticity in Vision Transformers, revealing that standard re-initialization methods fail to restore adaptability while update-regulation approaches succeed, leading to the proposal of ARROW, a geometry-aware optimizer that preserves plasticity by adaptively reshaping gradient directions based on online curvature estimates.
This paper demonstrates that a single, subject- and posture-agnostic time-varying cost function, efficiently estimated via the Minimal Observation Inverse Reinforcement Learning (MO-IRL) algorithm, can accurately predict human reaching movements by revealing a unified optimality principle dominated by joint-acceleration regulation.
This paper proposes a deep learning framework for MU-MISO precoding that leverages complex projective space parameterizations to eliminate global phase redundancies, thereby achieving superior sum-rate performance and generalization compared to conventional real or complex-valued representations.
This paper proposes a Physics Informed Neural Network framework with multi-head linear layers and orthogonality constraints to construct robust, interpretable low-dimensional embeddings for the viscous Burgers' equation, demonstrating that a small number of latent modes effectively capture the solution space's dominant dynamics.
This study demonstrates that for pasture biomass regression on scarce agricultural data, prioritizing high-quality backbone pretraining and utilizing simple local fusion modules significantly outperforms complex global architectures like SSMs and cross-view attention transformers, a phenomenon termed "fusion complexity inversion."
This paper proposes a novel two-step transfer learning framework utilizing bi-level optimization to train a universal feature-extractor and a task-specific domain-adapter, enabling high-quality image reconstruction in data-scarce scenarios by effectively leveraging diverse cross-domain data.
This paper introduces Gradient Iterated Temporal-Difference (GTD) learning, a novel algorithm that modifies iterated TD by computing gradients over moving targets to achieve the stability of gradient methods while matching the competitive learning speed of semi-gradient methods across diverse benchmarks like Atari games.
This paper introduces "Guess & Guide," a lightweight, gradient-free method for zero-shot diffusion guidance in Bayesian inverse problems that eliminates the computational burden of vector-Jacobian products while achieving state-of-the-art performance and Pareto optimality.
The paper introduces ReGEN-TAD, an interpretable generative framework that combines a convolutional-transformer architecture with econometric diagnostics to detect anomalies in high-dimensional financial time series by aggregating multiple signals into a unified, label-free score that enables economically coherent factor-level attribution.
This paper presents an end-to-end, viewpoint-agnostic grasping pipeline for mobile legged manipulators that leverages vision-language models and partial observation compensation to achieve robust, language-guided object selection and safe execution in cluttered environments, outperforming view-dependent baselines with a 90% success rate.
Inspired by biological sleep, the paper introduces Sleep Replay Consolidation (SRC), a post-training method that selectively replays internal representations to improve artificial neural network calibration and trustworthiness without requiring supervised retraining.