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Arxiv
15h
87
Image Credit: Arxiv
Principal Component Flow Map Learning of PDEs from Incomplete, Limited, and Noisy Data
- Researchers have developed a computational technique for modeling the evolution of dynamical systems in a reduced basis.
- The focus of the study is on modeling partially-observed partial differential equations (PDEs) on high-dimensional non-uniform grids.
- The technique addresses the limitations of previous work by considering noisy and limited data, simulating real-world data collection scenarios.
- By leveraging recent advancements in PDE modeling, the researchers propose a neural network structure that is suitable for modeling PDEs with noisy and limited data.
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Arxiv
15h
256
Image Credit: Arxiv
Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation
- Researchers propose a multi-source dynamic contrastive domain adaptation method (MS-DCDA) for EEG emotion recognition.
- The method leverages domain knowledge from multiple sources and uses dynamically weighted learning for optimal tradeoff between domain transferability and discriminability.
- The proposed MS-DCDA model achieves high accuracies in cross-subject and cross-session experiments on SEED and SEED-IV datasets.
- Insights from the study suggest greater emotional sensitivity in frontal and parietal brain lobes, with potential implications for mental health interventions and personalized medicine.
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Arxiv
15h
248
Image Credit: Arxiv
SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models
- Spiking neural networks (SNNs) have gained attention for their low energy consumption and temporal dynamics.
- Researchers have developed spiking state space models (SpikingSSMs) for long sequence learning.
- SpikingSSMs integrate neuronal dynamics with state space models and utilize sparse synaptic computation.
- The proposed SpikingSSM shows competitive performance on benchmark tasks and has potential as a low computation cost architecture for language models.
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Arxiv
15h
325
Image Credit: Arxiv
Unlocking Global Optimality in Bilevel Optimization: A Pilot Study
- Bilevel optimization is important in AI applications, but obtaining global optimality is challenging.
- Bilevel problems often lack a benign landscape and may have multiple local solutions.
- This paper explores global convergence theory for bilevel optimization.
- Two sufficient conditions for global convergence are presented, with proofs and experimental validation.
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Arxiv
15h
190
Image Credit: Arxiv
PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain
- Self-supervised learning is used for developing traversability models for off-road navigation.
- Existing methods utilize evidential deep learning to quantify model uncertainty.
- PIETRA is a self-supervised learning framework that integrates physics priors into evidential neural networks.
- PIETRA improves learning accuracy and navigation performance in environments with distribution shifts.
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Arxiv
15h
267
Image Credit: Arxiv
Data-driven decision-making under uncertainty with entropic risk measure
- The entropic risk measure is commonly used in high-stakes decision making to account for uncertain losses.
- An empirical entropic risk estimator is often biased and underestimates the true risk with limited data.
- A bootstrapping procedure is proposed to debias the empirical entropic risk estimator, improving risk estimation.
- The approach is applied to distributionally robust entropic risk minimization and insurance contract design problems.
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Arxiv
15h
223
Image Credit: Arxiv
TableRAG: Million-Token Table Understanding with Language Models
- Recent advancements in language models have enhanced their ability to reason with tabular data.
- TableRAG is a Retrieval-Augmented Generation (RAG) framework designed for LM-based table understanding.
- TableRAG leverages query expansion and schema/cell retrieval for efficient data encoding and precise retrieval.
- TableRAG achieves the highest retrieval quality and state-of-the-art performance on large-scale table understanding.
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13 Likes
Arxiv
15h
237
Image Credit: Arxiv
Variational Diffusion Posterior Sampling with Midpoint Guidance
- Diffusion models have shown potential in solving Bayesian inverse problems as priors.
- Sampling from denoising posterior distributions in diffusion models is challenging due to intractable terms.
- A novel approach is proposed that allows a trade-off between complexity of the intractable guidance term and prior transitions.
- The proposed approach is validated through experiments on inverse problems and applied to cardiovascular disease diagnosis.
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Arxiv
15h
54
Image Credit: Arxiv
Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
- Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology.
- Most studies have not considered the skewed degree distribution of genes, which complicates the application of directed graph embedding methods.
- To address this issue, the Cross-Attention Complex Dual Graph Embedding Model (XATGRN) is proposed.
- XATGRN effectively captures intricate gene interactions and accurately predicts regulatory relationships and their directionality.
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3 Likes
Arxiv
15h
84
Image Credit: Arxiv
Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data
- Variational Autoencoders (VAE) are widely used for dimensionality reduction of large-scale tabular and image datasets.
- The proposed model, LMMVAE, separates the VAE latent model into fixed and random parts to account for correlated data observations.
- LMMVAE improves squared reconstruction error and negative likelihood loss on unseen data.
- It also enhances performance in downstream tasks such as supervised classification.
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Arxiv
15h
325
Image Credit: Arxiv
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching
- Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation.
- Existing methods to speed up AR generation by generating multiple tokens at once are limited in capturing the output distribution due to token dependencies.
- Distilled Decoding (DD) uses flow matching to create a deterministic mapping from Gaussian distribution to the output distribution, enabling few-step generation.
- DD achieves promising results on ImageNet-256, enabling one-step generation with a speed-up of 6.3x for VAR and 217.8x for LlamaGen.
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19 Likes
Arxiv
15h
318
Image Credit: Arxiv
The Potential of Convolutional Neural Networks for Cancer Detection
- Convolutional Neural Networks (CNNs) have emerged as a powerful tool for analyzing and classifying medical images in cancer detection.
- This paper reviews recent studies on CNN models for detecting ten different types of cancer using diverse datasets.
- The paper compares and analyzes the performance and strengths of different CNN architectures in improving early detection.
- The study explores the feasibility of integrating CNNs into clinical settings as an early detection tool for cancer.
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Arxiv
15h
281
Image Credit: Arxiv
Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs
- Researchers have proposed a novel unsupervised learning scheme for accelerating the solution of mixed integer programming (MIP) problems.
- The scheme involves training an autoencoder (AE) in an unsupervised learning fashion using historical instances of optimal solutions to a parametric family of MIPs.
- By designing the AE architecture and utilizing its statistical implications, the researchers construct cutting plane constraints from the decoder parameters. These constraints improve the efficiency of solving new problem instances.
- The proposed approach demonstrates significant reduction in computational cost for solving mixed integer linear programming (MILP) problems, while maintaining high solution quality.
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Medium
15h
32
Image Credit: Medium
How Google’s Dominance Challenges OpenAI and Sora Without Breaking a Sweat
- Google's dominance in the AI industry poses challenges for competitors like OpenAI and Sora.
- Google's ecosystem and vast resources give it an edge in terms of data, infrastructure, and scale.
- The seamless integration of AI into everyday tools strengthens Google's position.
- Google's AI initiatives are built on years of experience and technological leadership, making it appear effortless.
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1 Like
Medium
17h
44
Image Credit: Medium
Exploring Quantum Computers:
- Quantum computers operate using qubits, which can exist in multiple states simultaneously.
- Two key phenomena, superposition and entanglement, make quantum computers unique.
- Potential applications of quantum computers include breaking encryption, simulating molecular structures, analyzing climate changes, and enhancing AI systems.
- Quantum computers face challenges such as the need for stable environments, high costs, and limited accessibility.
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