8660 papers
This paper introduces XFactor, the first geometry-free self-supervised model that achieves true novel view synthesis by disentangling camera pose from scene content through a novel augmentation scheme, thereby enabling pose transferability across different 3D scenes without relying on explicit 3D inductive biases.
This paper introduces CBF-RL, a framework that integrates Control Barrier Functions directly into the reinforcement learning training process to internalize safety constraints, thereby enabling safe, robust, and filter-free deployment of policies in real-world scenarios like humanoid robot navigation.
This paper introduces B-ODIL, a Bayesian extension of the Optimization of a Discrete Loss (ODIL) method that integrates PDE-based prior knowledge with data likelihood to solve inverse problems with quantified uncertainties, demonstrating its effectiveness through synthetic benchmarks and a clinical application for estimating brain tumor concentration from MRI scans.
The paper introduces Schrödinger Bridge Mamba (SBM), a novel one-step speech enhancement model that synergizes the Schrödinger Bridge training paradigm with the Mamba architecture to achieve superior denoising and dereverberation performance with real-time feasibility, outperforming strong generative and discriminative baselines.
This paper demonstrates that existing alignment-based defenses against control-flow hijacking in multi-agent systems are vulnerable to evasion due to inherent safety-functionality conflicts and limited context visibility, and proposes ControlValve, a new defense mechanism that enforces control-flow integrity and least privilege through permitted control-flow graphs and contextual rules.
This paper theoretically demonstrates that in overparameterized two-layer ReLU networks trained below the edge of stability, data geometry dictates generalization by determining whether gradient descent learns shared patterns (for data hard to shatter) or favors memorization (for data easily shattered).
This paper introduces a principled framework for testing excessive influence in linear least-squares models by deriving exact influence formulas and their corresponding extreme value distributions, thereby enabling rigorous hypothesis tests to distinguish between natural sampling variation and problematic data subsets across various scientific domains.
LLEMA is a unified framework that integrates large language models with chemistry-informed evolutionary rules and memory-based refinement to efficiently discover chemically plausible, stable, and multi-objective optimized materials, outperforming existing generative and LLM-only baselines across diverse application domains.
This paper proposes an auto-adaptive sampling method for Physics Informed Neural Networks (PINNs) that utilizes arbitrary problem-specific heuristics to accurately resolve interfacial regions in Allen-Cahn equations without post-hoc resampling, demonstrating superior performance over residual-adaptive frameworks.
The paper introduces FMint-SDE, a multimodal foundation model based on decoder-only transformers that leverages in-context learning and error correction to provide a general-purpose, efficient, and accurate solution for simulating stochastic differential equations across diverse scientific domains.
MotionStream is a real-time video generation framework that distills a bidirectional teacher model into a causal student using Self Forcing with Distribution Matching Distillation and sliding-window attention with attention sinks, enabling sub-second, infinite-length streaming generation with interactive motion controls on a single GPU.
CytoNet is a foundation model trained on a million unlabeled histological patches from ten human brains that encodes complex cellular patterns into meaningful features, enabling scalable analysis of cortical microarchitecture and linking cellular structure to functional organization through various downstream applications.
The paper proposes Correctness-Relative Policy Optimization (CoRPO), a modification to Group-Relative Policy Optimization (GRPO) that clips the advantage baseline to a correctness threshold to prevent reinforcing incorrect solutions, thereby significantly improving the model's generalization and cross-domain reasoning capabilities.
This paper proposes SPOT, an end-to-end deep learning framework that jointly optimizes phase-time array beamforming and user position estimation to achieve high-accuracy single-shot localization with significantly reduced overhead compared to existing methods.
ReCast is a lightweight, reliability-aware time series forecasting framework that utilizes a learnable codebook to encode recurring local patterns and a dual-path architecture to model residuals, employing a distributionally robust optimization strategy to ensure robustness against distribution shifts and non-stationarity.
This paper introduces FlashCache, a frequency-domain-guided KV cache compression framework that identifies and preserves critical "Outlier KVs" while leveraging low-pass filtering and dynamic budget allocation to achieve significant inference speedups and memory reduction in multimodal large language models without compromising performance.
The paper introduces Observer-Actor (ObAct), a novel active vision imitation learning framework for dual-arm robots that dynamically assigns one arm to construct a 3D Gaussian Splatting representation and identify optimal viewing angles for the other arm, thereby significantly enhancing policy robustness and performance by reducing occlusions compared to static-camera setups.
This paper introduces CycleChemist, a dual-pronged machine learning framework that leverages a new large-scale dataset (OPV2D) to simultaneously predict organic photovoltaic performance and generate synthetically accessible high-efficiency donor-acceptor materials through an integrated predictive and generative approach.
This paper proposes a novel Physics-Informed U-Net-LSTM framework that integrates physical laws with deep learning to overcome the computational limitations of traditional Finite Element Methods and the generalization issues of purely data-driven models, thereby enabling accurate and efficient prediction of nonlinear structural seismic responses.
This paper introduces DPAC, a diffusion guidance method that projects adversarial gradients onto the tangent space of iso-density surfaces to minimize path-space KL divergence and control energy, thereby theoretically and empirically achieving higher sample quality (lower FID) while maintaining target classification success.