8488 papers
This paper introduces Bias-Invariant Subnetwork Extraction (BISE), a method that identifies and isolates fair, bias-agnostic subnetworks within standard pre-trained models through pruning, enabling effective bias mitigation without retraining or additional unbiased data.
This paper demonstrates that pretraining tokenizers with an autoencoding objective before training dynamics models significantly enhances the computational efficiency and accuracy of physics foundation models, particularly when the pretraining data aligns with the downstream physical system.
This paper introduces the first coupled robustness verification framework for heatmap-based keypoint detectors that uses a mixed-integer linear program to jointly bound deviations across all keypoints, thereby providing sound and less conservative guarantees than prior decoupled methods.
This paper introduces behavior-decomposed linear dynamical systems (b-dLDS), a novel modeling approach that disentangles behavior-related neural dynamics from internal computations in large-scale brain recordings, demonstrating superior performance over existing supervised models and successfully scaling to tens of thousands of neurons in zebrafish hindbrain data.
The paper introduces RACAS, a robot-agnostic agentic system that uses natural language communication between LLM/VLM-based modules to control diverse robotic platforms without requiring code modifications or retraining, successfully demonstrating its effectiveness across wheeled, multi-jointed, and underwater robots.
This paper proposes a domain-agnostic framework to identify the most probable characteristics of an attacker from an observed data-manipulation attack, demonstrating that such identification enables more effective exogenous mitigation and improves the performance of learning-based defenses.
This paper introduces interpolated FID (iFID), a novel metric that achieves a strong correlation with diffusion generation FID by interpolating latent representations between dataset samples and their nearest neighbors, thereby overcoming the limitations of traditional reconstruction FID in predicting generative model quality.
This paper introduces Implicit Error Counting (IEC), a reference-free reinforcement learning post-training method that enumerates and weights errors to generate rewards, demonstrating superior performance over Rubrics as Rewards (RaR) in virtual try-on tasks where multiple valid outputs exist and ideal reference answers are unavailable.
This paper demonstrates that integrating graph-based embeddings of on-court and off-court player data into tabular datasets significantly improves the accuracy of supervised machine learning models for predicting NBA player salaries, particularly for veterans and high-earning outliers where traditional methods fail.
This paper proposes a reinforcement learning framework with a probabilistic reward function and a Gaussian baseline to discover power-flow network configurations that yield a significantly higher number of equilibrium points than current computational algebra methods can identify.
This paper demonstrates that in random feature ridge regression, a strong student model trained on imperfect labels from a weak teacher can achieve substantially improved scaling laws and even reach minimax optimal rates, regardless of whether the teacher's own test error decays with sample size.
This paper investigates parallelization strategies for deploying dense LLMs, demonstrating that while Tensor Parallelism optimizes latency and Pipeline Parallelism enhances throughput, a hybrid approach allows for effective control over the inherent latency-throughput tradeoff to meet specific application requirements.
This paper establishes a formal correspondence between Moore machines and state-space models to demonstrate that initializing continuous SSMs with symbolically learned automata significantly improves training efficiency and accuracy compared to random initialization, thereby effectively leveraging symbolic inductive bias for learning complex systems.
This paper establishes a geometric framework using principal fiber bundles to identify fundamental obstructions in learning differential equations from temporal Random Dot Product Graphs, characterizing the interplay between gauge ambiguity, spectral gaps, and holonomy while demonstrating that symmetric dynamics can resolve gauge issues to enable vector field recovery.
This paper argues that while AI promises to revolutionize climate information, its current reliance on Global North-dominated infrastructure and biased data risks exacerbating global inequality, necessitating a shift toward data-centric development, shared digital public infrastructure, and co-produced knowledge to ensure equitable outcomes.
This paper demonstrates that unsupervised domain adaptation, specifically through minimizing maximum mean discrepancy (MMD) between synthetic and unlabeled real-world data, significantly improves the generalization and testing accuracy of machine learning models for radioisotope identification in gamma spectroscopy.
This paper challenges prior claims of Best-of-N's suboptimality by demonstrating that, under practical assumptions and when evaluated via win-rate rather than expected reward, properly tuned Best-of-N is both statistically and computationally optimal, while also proposing a simple variant that eliminates reward hacking without sacrificing performance.
This paper introduces Icarus, a deep learning-based all-weather sky model that overcomes the limitations of existing methods in handling full dynamic range and class-imbalanced solar regions to generate photorealistic, user-controllable environment maps for accurate Image-Based Lighting.
The paper introduces MIRACL, a novel hierarchical Meta-MORL framework that enables few-shot generalization and efficient adaptation for multi-objective multi-echelon supply chain optimization by decomposing tasks into structured subproblems and employing a Pareto-based strategy to achieve superior performance over conventional baselines.
This paper introduces Score-Guided Proximal Projection (SGPP), a unified geometric framework that reformulates Rectified Flow editing as a proximal optimization problem to overcome the limitations of existing inversion and sampling methods by theoretically guaranteeing manifold convergence while enabling a continuous, training-free trade-off between identity preservation and generative flexibility.