Search Results for author: Johan Bjorck

Found 13 papers, 3 papers with code

Towards Deeper Deep Reinforcement Learning with Spectral Normalization

no code implementations NeurIPS 2021 Johan Bjorck, Carla P. Gomes, Kilian Q. Weinberger

In this paper we investigate how RL agents are affected by exchanging the small MLPs with larger modern networks with skip connections and normalization, focusing specifically on actor-critic algorithms.

reinforcement-learning Reinforcement Learning (RL)

Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision

no code implementations26 Feb 2021 Johan Bjorck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger

Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning.

Continuous Control reinforcement-learning +1

Dataset Curation Beyond Accuracy

no code implementations1 Jan 2021 Johan Bjorck, Carla P Gomes

Neural networks are known to be data-hungry, and collecting large labeled datasets is often a crucial step in deep learning deployment.

Self-Driving Cars

Understanding Decoupled and Early Weight Decay

no code implementations27 Dec 2020 Johan Bjorck, Kilian Weinberger, Carla Gomes

We also show how the growth of network weights is heavily influenced by the dataset and its generalization properties.

Star-Convexity in Non-Negative Matrix Factorization

no code implementations25 Sep 2019 Johan Bjorck, Carla Gomes, Kilian Weinberger

Non-negative matrix factorization (NMF) is a highly celebrated algorithm for matrix decomposition that guarantees strictly non-negative factors.

Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant

no code implementations25 Feb 2019 Johan Bjorck, Brendan H. Rappazzo, Di Chen, Richard Bernstein, Peter H. Wrege, Carla P. Gomes

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa.

Understanding Batch Normalization

no code implementations NeurIPS 2018 Johan Bjorck, Carla Gomes, Bart Selman, Kilian Q. Weinberger

Batch normalization (BN) is a technique to normalize activations in intermediate layers of deep neural networks.

Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery

1 code implementation3 Oct 2016 Yexiang Xue, Junwen Bai, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Johan Bjorck, Liane Longpre, Santosh K. Suram, Robert B. van Dover, John Gregoire, Carla P. Gomes

A key problem in materials discovery, the phase map identification problem, involves the determination of the crystal phase diagram from the materials' composition and structural characterization data.

Vocal Bursts Intensity Prediction

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