Search Results for author: Catalin Ionescu

Found 11 papers, 5 papers with code

Hierarchical Perceiver

no code implementations22 Feb 2022 Joao Carreira, Skanda Koppula, Daniel Zoran, Adria Recasens, Catalin Ionescu, Olivier Henaff, Evan Shelhamer, Relja Arandjelovic, Matt Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle

General perception systems such as Perceivers can process arbitrary modalities in any combination and are able to handle up to a few hundred thousand inputs.

Making Sense of Reinforcement Learning and Probabilistic Inference

no code implementations ICLR 2020 Brendan O'Donoghue, Ian Osband, Catalin Ionescu

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.

reinforcement-learning

Unsupervised Control Through Non-Parametric Discriminative Rewards

no code implementations ICLR 2019 David Warde-Farley, Tom Van de Wiele, tejas kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih

Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research.

reinforcement-learning

Using Fast Weights to Attend to the Recent Past

4 code implementations NeurIPS 2016 Jimmy Ba, Geoffrey Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu

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

no code implementations ICCV 2015 Catalin Ionescu, Orestis Vantzos, Cristian Sminchisescu

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

1 code implementation25 Sep 2015 Catalin Ionescu, Orestis Vantzos, Cristian Sminchisescu

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

no code implementations CVPR 2014 Catalin Ionescu, Joao Carreira, Cristian Sminchisescu

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.

3D Human Pose Estimation

Human3.6m: Large scale datasets and predictive methods for 3D human sensing in natural environments

1 code implementation IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 36 , Issue: 7 , July 2014 ) 2013 Catalin Ionescu, Dragos Papava, Vlad Olaru, Cristian Sminchisescu

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.

3D Human Pose Estimation Mixed Reality

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