A team from Google Research proposes prediction depth, a new measure of example difficulty determined from hidden embeddings. Their study reveals the surprising fact that the prediction depth of a given input has strong connections to a model’s uncertainty, confidence, accuracy and speed of learning for that data point.
Researchers from Google conduct a survey on how to make Deep Learning models smaller, faster, and better. The team focuses on core areas of model efficiency, from modelling techniques to hardware support, and open-sources an experiment-based guide and code to help practitioners optimize their model training and deployment.
A research team from ETH Zürich presents an overview of priors for (deep) Gaussian processes, variational autoencoders and Bayesian neural networks. The researchers propose that well-chosen priors can achieve theoretical and empirical properties such as uncertainty estimation, model selection and optimal decision support; and provide guidance on how to choose them.
Twitter Chief Scientist Michael Bronstein, Joan Bruna from New York University, Taco Cohen from Qualcomm AI and Petar Veličković from DeepMind publish a paper that aims to geometrically unify the typical architectures of CNNs, GNNs, LSTMs, Transformers, etc. from the perspective of symmetry and invariance to build an “Erlangen Programme” for deep neural networks.
Researchers from Carnegie Mellon University, the University of Texas at Austin and Facebook AI propose a novel paradigm to optimize widths for each CNN layer. The method is compatible across various width optimization algorithms and networks and achieves up to a 320x reduction in width optimization overhead without compromising top-1 accuracy on ImageNet.
A research team from ETH and UC Berkeley proposes a Deep Reward Learning by Simulating the Past (Deep RLSP) algorithm that represents rewards directly as a linear combination of features learned through self-supervised representation learning and enables agents to simulate human actions backwards in time to infer what they must have done.
A research team from Technical University of Munich, Google, Nvidia and LMU München proposes CodeTrans, an encoder-decoder transformer model which achieves state-of-the-art performance on six tasks in the software engineering domain, including Code Documentation Generation, Source Code Summarization, Code Comment Generation, etc.
The 16th European Conference on Computer Vision (ECCV) kicked off on Sunday as a fully online conference. In the Conference Opening Session this morning, the ECCV organizing committee announced the conference’s paper submission stats and Best Paper selections.