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Found 18 papers, 6 papers with code
Robust off-policy Reinforcement Learning via Soft Constrained Adversary
Recently, robust reinforcement learning (RL) methods against input observation have garnered significant attention and undergone rapid evolution due to RL's potential vulnerability.
A Simple, Solid, and Reproducible Baseline for Bridge Bidding AI
Contract bridge, a cooperative game characterized by imperfect information and multi-agent dynamics, poses significant challenges and serves as a critical benchmark in artificial intelligence (AI) research.
A Batch Sequential Halving Algorithm without Performance Degradation
In this paper, we investigate the problem of pure exploration in the context of multi-armed bandits, with a specific focus on scenarios where arms are pulled in fixed-size batches.
End-to-End Policy Gradient Method for POMDPs and Explainable Agents
We proposed an RL algorithm that estimates the hidden states by end-to-end training, and visualize the estimation as a state-transition graph.
Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
We propose Pgx, a suite of board game reinforcement learning (RL) environments written in JAX and optimized for GPU/TPU accelerators.
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition
The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases.
MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information.
UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images
The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs) that enable automated image segmentation.
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation
Neural sequence generation is commonly approached by using maximum- likelihood (ML) estimation or reinforcement learning (RL).
Efficient Diverse Ensemble for Discriminative Co-Tracking
To remove this redundancy and have an effective ensemble learning, it is critical for the committee to include consistent hypotheses that differ from one-another, covering the version space with minimum overlaps.
Neural Sequence Model Training via $α$-divergence Minimization
We propose a new neural sequence model training method in which the objective function is defined by $\alpha$-divergence.
Active Collaborative Ensemble Tracking
However, by updating all of the ensemble using a shared set of samples and their final labels, such diversity is lost or reduced to the diversity provided by the underlying features or internal classifiers' dynamics.
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets.
Efficient Version-Space Reduction for Visual Tracking
To cope with variations of the target shape and appearance, the classifier is updated online with different samples of the target and the background.
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
We also introduce a budgeting mechanism which prevents the unbounded growth in the number of examples in the first detector to maintain its rapid response.
Distributional Smoothing with Virtual Adversarial Training
We propose local distributional smoothness (LDS), a new notion of smoothness for statistical model that can be used as a regularization term to promote the smoothness of the model distribution.
Deep learning of fMRI big data: a novel approach to subject-transfer decoding
Our PSA successfully visualized the subject-independent features contributing to the subject-transferability of the trained decoder.
Principal Sensitivity Analysis
We then visualize the PSMs to demonstrate the PSA's ability to decompose the knowledge acquired by the trained classifiers.
