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Found 11 papers, 6 papers with code
HiP: Hierarchical Perceiver
This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video.
Perceiver IO: A General Architecture for Structured Inputs & Outputs
A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible.
Ranked #1 on
Optical Flow Estimation
on KITTI 2015
(Average End-Point Error metric)
Making Sense of Reinforcement Learning and Probabilistic Inference
Reinforcement learning (RL) combines a control problem with statistical estimation: The system dynamics are not known to the agent, but can be learned through experience.
Unsupervised Learning of Object Keypoints for Perception and Control
In this work we aim to learn object representations that are useful for control and reinforcement learning (RL).
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning
Exploration in environments with sparse rewards is a key challenge for reinforcement learning.
Unsupervised Control Through Non-Parametric Discriminative Rewards
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research.
Using Fast Weights to Attend to the Recent Past
Until recently, research on artificial neural networks was largely restricted to systems with only two types of variable: Neural activities that represent the current or recent input and weights that learn to capture regularities among inputs, outputs and payoffs.
Matrix Backpropagation for Deep Networks With Structured Layers
Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features.
Training Deep Networks with Structured Layers by Matrix Backpropagation
Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features.
Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation
Recently, the emergence of Kinect systems has demonstrated the benefits of predicting an intermediate body part labeling for 3D human pose estimation, in conjunction with RGB-D imagery.
Human3.6m: Large scale datasets and predictive methods for 3D human sensing in natural environments
We introduce a new dataset, Human3. 6M, of 3. 6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms.
