3874 papers
This paper proposes SDR-GAIN, a novel real-time framework that utilizes self-supervised adversarial learning on keypoint coordinate distributions to accurately reconstruct occluded pedestrian poses for autonomous driving, outperforming existing methods in both accuracy and inference speed.
This paper proposes TSformer-SA, a novel framework that integrates a temporal-spectral fusion transformer with subject-specific adapters and cross-view consistency learning to significantly enhance RSVP-BCI decoding performance while minimizing the training data and preparation time required for new subjects.
The paper proposes PnLCalib, an optimization-based calibration pipeline for sports field registration that leverages a 3D soccer field model, keypoints, and a novel line-based refinement module to achieve superior accuracy and robustness in diverse broadcast scenarios compared to traditional search-based methods.
The paper proposes DP-IQA, a novel blind image quality assessment method that leverages the robust perceptual priors of pre-trained Stable Diffusion models and distills them into a lightweight CNN to achieve state-of-the-art generalization on in-the-wild datasets with limited training data.
This paper introduces ScenarioFuzz, a novel scenario-based fuzzing methodology that leverages historical test data, map networks, and graph neural networks to autonomously generate and optimize high-risk scenarios, significantly reducing testing time while uncovering numerous safety-critical bugs in autonomous driving systems.
This paper proposes and evaluates a distributed multi-agent Q-learning solution for HD map updates in vehicular networks that reduces computational burdens and compatibility issues while significantly improving time latencies across various traffic scenarios compared to single-agent approaches.
This paper proposes CoHet, a novel algorithm that leverages Graph Neural Network-driven intrinsic rewards to enable effective decentralized learning and cooperation among heterogeneous multi-agent systems despite challenges like partial observability and reward sparsity, demonstrating superior performance over state-of-the-art methods in standard benchmarks.
This paper introduces Sparse Variational Student-t Processes (SVTP), a scalable framework that extends sparse inducing point methods to Student-t processes via novel inference algorithms and natural gradient optimization, achieving superior robustness to outliers and heavy-tailed data with significantly faster convergence and lower prediction error compared to sparse Gaussian processes on large datasets.
This paper introduces a unified framework that models quantization and sparsification as additive noise to derive a principled, noise-corrective gradient path, enabling the stable training of neural networks at arbitrary low precisions and sparsity levels without relying on heuristic estimators like the Straight-Through Estimator.
The paper introduces DRUPI (Dataset Condensation using Privileged Information), a framework that enhances dataset condensation by synthesizing auxiliary privileged information, such as feature or attention labels, alongside reduced data to significantly improve model training performance across various benchmarks.
LayoutDreamer is a novel framework that leverages 3D Gaussian Splatting and physics-guided scene graphs to generate high-quality, physically plausible, and controllable text-to-3D compositional scenes, achieving state-of-the-art performance in multi-object generation.
This paper introduces Astromer 2, an enhanced foundational model for light curve analysis that leverages self-supervised pre-training on 1.5 million MACHO survey observations and weighted per-sample embeddings to significantly outperform its predecessor and prior models in classification tasks, particularly when trained with limited labeled data.
This paper addresses the sensitivity of semivalue-based data valuation to utility selection by introducing a "spatial signature" framework that embeds data points into a lower-dimensional space, enabling a practical methodology to quantify and ensure the robustness of valuation results against utility variations.
This paper presents MKE-Coder, a novel framework that improves automatic ICD coding for Chinese electronic medical records by integrating multi-axial disease knowledge with a clinical evidence verification module to address challenges in information extraction and code validity.
The paper introduces LLM-Advisor, a prompt-based framework that leverages large language models as non-decisive post-processing advisors to significantly improve the cost efficiency of path planning across diverse terrains without modifying underlying planners, while addressing hallucination risks and demonstrating superior performance over zero-shot LLM approaches.
The paper introduces HyConEx, a novel deep hypernetwork-based classifier for tabular data that uniquely integrates prediction and explanation by simultaneously generating class labels and local counterfactual examples to interpret model decisions.
This paper proposes a theoretically grounded approach using category theory to introduce an "explaining functor" that structurally preserves logical entailment, thereby ensuring the consistency and fidelity of logic-based explanations for opaque AI models and overcoming the limitations of current heuristic post-hoc methods.
GateLens is a reasoning-enhanced LLM agent that utilizes Relational Algebra as a formal intermediate representation to bridge the gap between natural language and executable code, enabling fast, transparent, and highly accurate analysis of complex tabular data in automotive software release analytics without requiring few-shot examples or complex agent orchestration.
This paper critiques the prevalent reliance on fixed-threshold metrics in machine learning evaluation by advocating for a consequentialist framework that prioritizes proper scoring rules like the Brier score, supported by a new decision-theoretic mapping, a practical Python package called `briertools`, and a clipped Brier score variant to bridge the gap between theoretical utility and current practices.
This paper introduces MCP Bridge, a lightweight, LLM-agnostic RESTful proxy that enables Model Context Protocol servers to run in resource-constrained environments with enhanced security, while also presenting a fine-tuned Qwen3 model that achieves state-of-the-art performance on the MCPToolBench++ benchmark through advanced reinforcement learning techniques.