7673 papers
This paper presents a neural network-based correlation model demonstrating similar trends between COVID-19 and Dengue cases, which is extended into an LSTM framework to predict Dengue infections in regions with insufficient data by leveraging COVID-19 statistics and external factors.
This paper proposes a scalable and robust Graph Neural Network (GNN) framework operating on augmented factor graphs to achieve fast, accurate linear state estimation in power systems using PMU data, effectively mitigating the impact of measurement failures while maintaining linear computational complexity.
This paper introduces Ancestral GFlowNet (AGFN), a diversity-seeking reinforcement learning algorithm that enables distributional inference over ancestral graphs by iteratively refining its policy through Bayesian aggregation of both ex-ante and uncertain ex-post expert feedback, ultimately converging to the true causal structure even when expert responses are noisy or conflicting.
This paper introduces a unified framework for learning unknown objects from arbitrary linear samples using general nonlinear model classes, establishing near-optimal generalization bounds based on the model's variation and complexity while recovering and extending existing results in areas like compressed sensing and matrix sketching.
This paper derives model-agnostic theoretical lower-bounds for the energy-to-solution metric of ideal neuromorphic learning-in-memory optimizers by analyzing their out-of-equilibrium thermodynamics, demonstrating how matching memory dynamics to optimization processes can overcome energy bottlenecks associated with memory writes and consolidation in large-scale AI workloads.
This paper proposes a pose-aware in-context visual learning (PA-ICVL) framework that enhances Vision-Language Models' ability to detect semantic structural visual hallucinations in non-photorealistic cartoon images by integrating pose information alongside RGB data, achieving significant performance improvements over RGB-only baselines.
BInD is a knowledge-guided diffusion model that co-generates molecules and their interactions with target proteins to achieve balanced multi-objective optimization in structure-based drug design, outperforming existing methods across key criteria like binding affinity, molecular properties, and local geometry.
This paper proposes a novel two-stage active learning pipeline for automatic speech recognition that combines unsupervised x-vector clustering with a supervised Bayesian batch selection method to efficiently identify diverse and informative samples, thereby significantly reducing labeling effort while improving model performance across various test conditions.
This paper provides a comprehensive review and formal specification of Predictive Coding Networks (PCNs), highlighting their biological plausibility, computational efficiency through inference learning, and versatility as a probabilistic framework that extends beyond traditional backpropagation-based neural networks.
This paper argues that while Large Language Models lack the autonomy required for genuine agency due to their absence of self-generated norms, goals, and embodied interaction, they function as transformative "linguistic automata" that, through a unique human-machine coupling, enable new "midtended" forms of intentional agency.
The paper proposes PACE, a novel parameter-efficient fine-tuning method that enhances model generalization and preserves pre-trained knowledge by employing consistency regularization with multiplicative noise to implicitly reduce gradient norms and align fine-tuned models with their pre-trained counterparts.
FALCON is a unified self-supervised pretraining framework for UAV action recognition that overcomes spatial imbalance in aerial footage by combining object-aware masked autoencoding with object-centric dual-horizon future reconstruction, achieving superior accuracy and faster inference without requiring additional preprocessing at test time.
This paper proposes a self-supervised, lightweight deep learning method that leverages self-consistency learning and inter-component correlations to achieve high-quality reconstruction of irregularly acquired seismic data without requiring external training datasets.
The paper proposes Ditto, a real-time diffusion-based framework for controllable talking head synthesis that achieves fine-grained motion control and low-latency streaming inference by optimizing a motion-space diffusion transformer to resolve issues of motion-identity disentanglement and internal representation discrepancies.
This paper proposes a sparse -regularized quadratic surface support vector machine (QSVM) to address overfitting and interpretability issues in nonlinear classification, introducing a penalty decomposition algorithm with provable optimality guarantees that achieves competitive performance and sparsity on both benchmark and real-world credit scoring datasets.
This survey provides a comprehensive overview of the emerging ecosystem of large language models and tools that support researchers across the scientific lifecycle, covering key tasks from literature search and idea generation to content creation, experimentation, and evaluation, while addressing associated datasets, methods, limitations, and ethical concerns.
The paper introduces FragFM, a hierarchical framework utilizing fragment-level discrete flow matching and a stochastic fragment bag strategy to achieve efficient, scalable, and property-controllable molecular generation, validated through a new Natural Product Generation (NPGen) benchmark where it outperforms existing atom-based methods.
This paper introduces SysDPO, a framework that aligns complex, multi-component Compound AI Systems with human preferences by modeling them as Directed Acyclic Graphs and extending Direct Preference Optimization to overcome the challenges of non-differentiable interactions and the difficulty of translating system-level preferences to component levels.
This paper investigates the adversarial robustness of partitioned quantum classifiers by demonstrating that perturbations targeting circuit partitioning techniques, such as wire cutting or teleportation, are equivalent to implementing adversarial gates within intermediate layers, a relationship analyzed through both theoretical and experimental perspectives.
This paper introduces Context-Aware Priority Sampling (CAPS), a novel imitation learning method that leverages VQ-VAEs to cluster and re-balance training data, thereby improving the generalization, driving score, and success rate of autonomous driving systems in CARLA simulations.