Timezone: »
A Quantum Field Theory of Representation Learning Robert Bamler (University of California at Irvine)*; Stephan Mandt (University of California, Irivine)
Covariance in Physics and Convolutional Neural Networks Miranda Cheng (University of Amsterdam)*; Vassilis Anagiannis (University of Amsterdam); Maurice Weiler (University of Amsterdam); Pim de Haan (University of Amsterdam); Taco S. Cohen (Qualcomm AI Research); Max Welling (University of Amsterdam)
Scale Steerable Filters for Locally Scale-Invariant Convolutional Neural Networks Rohan Ghosh (National University of Singapore)*; Anupam Gupta (National University of Singapore)
Towards a Definition of Disentangled Representations Irina Higgins (DeepMind)*; David Amos (DeepMind); Sebastien Racaniere (DeepMind); David Pfau (); Loic Matthey (DeepMind); Danilo Jimenez Rezende (Google DeepMind)
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak (Google Brain)*; Lechao Xiao (Google Brain); Jaehoon Lee (Google Brain); Yasaman Bahri (Google Brain); Greg Yang (Microsoft Research AI); Jiri Hron (University of Cambridge); Daniel Abolafia (Google Brain); Jeffrey Pennington (Google Brain); Jascha Sohl-Dickstein (Google Brain)
Finite size corrections for neural network Gaussian processes Joseph M Antognini (Whisper AI)*
Pathological Spectrum of the Fisher Information Matrix in Deep Neural Networks Ryo Karakida (National Institute of Advanced Industrial Science and Technology)*; Shotaro Akaho (AIST); Shun-ichi Amari (RIKEN)
Inferring the quantum density matrix with machine learning Kyle Cranmer (New York University); Siavash Golkar (NYU)*; Duccio Pappadopulo (Bloomberg)
Jet grooming through reinforcement learning Frederic Dreyer (University of Oxford)*; Stefano Carrazza (University of Milan)
Author Information
Roman Novak (Google Brain)
Frederic Dreyer (University of Oxford)
Theoretical physics researcher working on quantum chromodynamics and applications of machine learning for the LHC.
Siavash Golkar (New York University)
Irina Higgins (DeepMind)

Irina Higgins is a research scientist at DeepMind, where she works in the Froniers team. Her work aims to bring together insights from the fields of neuroscience and physics to advance general artificial intelligence through improved representation learning. Before joining DeepMind, Irina was a British Psychological Society Undergraduate Award winner for her achievements as an undergraduate student in Experimental Psychology at Westminster University, followed by a DPhil at the Oxford Centre for Computational Neuroscience and Artificial Intelligence, where she focused on understanding the computational principles underlying speech processing in the auditory brain. During her DPhil, Irina also worked on developing poker AI, applying machine learning in the finance sector, and working on speech recognition at Google Research.
Joe Antognini (Whisper AI)
Ryo Karakida (National Institute of AIST)
Rohan Ghosh (National University of Singapore)
More from the Same Authors
-
2023 Poster: Understanding Gradient Regularization in Deep Learning: Efficient Finite-Difference Computation and Implicit Bias »
Ryo Karakida · Tomoumi Takase · Tomohiro Hayase · Kazuki Osawa -
2022 Poster: Fast Finite Width Neural Tangent Kernel »
Roman Novak · Jascha Sohl-Dickstein · Samuel Schoenholz -
2022 Spotlight: Fast Finite Width Neural Tangent Kernel »
Roman Novak · Jascha Sohl-Dickstein · Samuel Schoenholz -
2022 Poster: Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling »
Jiri Hron · Roman Novak · Jeffrey Pennington · Jascha Sohl-Dickstein -
2022 Spotlight: Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling »
Jiri Hron · Roman Novak · Jeffrey Pennington · Jascha Sohl-Dickstein -
2020 Poster: Infinite attention: NNGP and NTK for deep attention networks »
Jiri Hron · Yasaman Bahri · Jascha Sohl-Dickstein · Roman Novak -
2020 Tutorial: Representation Learning Without Labels »
S. M. Ali Eslami · Irina Higgins · Danilo J. Rezende -
2019 : Poster discussion »
Roman Novak · Maxime Gabella · Frederic Dreyer · Siavash Golkar · Anh Tong · Irina Higgins · Mirco Milletari · Joe Antognini · Sebastian Goldt · Adín Ramírez Rivera · Roberto Bondesan · Ryo Karakida · Remi Tachet des Combes · Michael Mahoney · Nicholas Walker · Stanislav Fort · Samuel Smith · Rohan Ghosh · Aristide Baratin · Diego Granziol · Stephen Roberts · Dmitry Vetrov · Andrew Wilson · César Laurent · Valentin Thomas · Simon Lacoste-Julien · Dar Gilboa · Daniel Soudry · Anupam Gupta · Anirudh Goyal · Yoshua Bengio · Erich Elsen · Soham De · Stanislaw Jastrzebski · Charles H Martin · Samira Shabanian · Aaron Courville · Shorato Akaho · Lenka Zdeborova · Ethan Dyer · Maurice Weiler · Pim de Haan · Taco Cohen · Max Welling · Ping Luo · zhanglin peng · Nasim Rahaman · Loic Matthey · Danilo J. Rezende · Jaesik Choi · Kyle Cranmer · Lechao Xiao · Jaehoon Lee · Yasaman Bahri · Jeffrey Pennington · Greg Yang · Jiri Hron · Jascha Sohl-Dickstein · Guy Gur-Ari -
2017 Poster: DARLA: Improving Zero-Shot Transfer in Reinforcement Learning »
Irina Higgins · Arka Pal · Andrei A Rusu · Loic Matthey · Christopher Burgess · Alexander Pritzel · Matthew Botvinick · Charles Blundell · Alexander Lerchner -
2017 Talk: DARLA: Improving Zero-Shot Transfer in Reinforcement Learning »
Irina Higgins · Arka Pal · Andrei A Rusu · Loic Matthey · Christopher Burgess · Alexander Pritzel · Matthew Botvinick · Charles Blundell · Alexander Lerchner