Publications

Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation

Divyat Mahajan

Ioannis Mitliagkas

Brady Neal

Vasilis Syrgkanis

2024-01-16

ICLR.cc/2024/Conference (spotlight)

openreview.net

Ensemble Distillation for Unsupervised Constituency Parsing

Behzad Shayegh

Yanshuai Cao

Xiaodan Zhu

Jackie Cheung

Lili Mou

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

Evaluating Representation Learning on the Protein Structure Universe

Arian Rokkum Jamasb

Alex Morehead

Chaitanya K. Joshi

Zuobai Zhang

Kieran Didi

Simon V Mathis

Charles Harris

Jian Tang

Jianlin Cheng

Pietro Lio

Tom Leon Blundell

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

Expected flow networks in stochastic environments and two-player zero-sum games

Marco Jiralerspong

Bilun Sun

Danilo Vucetic

Tianyu Zhang

Yoshua Bengio

Gauthier Gidel

Nikolay Malkin

2024-01-16

ICLR.cc/2024/Conference (poster)

doi.org

openreview.net

Ghost on the Shell: An Expressive Representation of General 3D Shapes

Zhen Liu

Yao Feng

Yuliang Xiu

Weiyang Liu

Liam Paull

Michael J. Black

Bernhard Schölkopf

2024-01-16

ICLR.cc/2024/Conference (oral)

doi.org

openreview.net

Hallucination Detection and Hallucination Mitigation: An Investigation

Junliang Luo

Tianyu Li

Di Wu

M. Jenkin

Steve Liu

Gregory Dudek

2024-01-16

ArXiv (preprint)

doi.org

arxiv.org

How connectivity structure shapes rich and lazy learning in neural circuits

Yuhan Helena Liu

Aristide Baratin

Jonathan Cornford

Stefan Mihalas

Eric Todd SheaBrown

Guillaume Lajoie

In theoretical neuroscience, recent work leverages deep learning tools to explore how some network attributes critically influence its learn… (see more)ing dynamics. Notably, initial weight distributions with small (resp. large) variance may yield a rich (resp. lazy) regime, where significant (resp. minor) changes to network states and representation are observed over the course of learning. However, in biology, neural circuit connectivity generally has a low-rank structure and therefore differs markedly from the random initializations generally used for these studies. As such, here we investigate how the structure of the initial weights — in particular their effective rank — influences the network learning regime. Through both empirical and theoretical analyses, we discover that high-rank initializations typically yield smaller network changes indicative of lazier learning, a finding we also confirm with experimentally-driven initial connectivity in recurrent neural networks. Conversely, low-rank initialization biases learning towards richer learning. Importantly, however, as an exception to this rule, we find lazier learning can still occur with a low-rank initialization that aligns with task and data statistics. Our research highlights the pivotal role of initial weight structures in shaping learning regimes, with implications for metabolic costs of plasticity and risks of catastrophic forgetting.

2024-01-16

ICLR.cc/2024/Conference (poster)

doi.org

openreview.net

Improving Intrinsic Exploration by Creating Stationary Objectives

Roger Creus Castanyer

Joshua Romoff

Glen Berseth

2024-01-16

ICLR.cc/2024/Conference (poster)

doi.org

openreview.net

Intelligent Switching for Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Glen Berseth

Sarath Chandar Anbil Parthipan

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

Intelligent Switching for Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Glen Berseth

Sarath Chandar Anbil Parthipan

In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resett… (see more)ing} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.

2024-01-16

ICLR.cc/2024/Conference (poster)

doi.org

openreview.net

Jointly-Learned Exit and Inference for a Dynamic Neural Network

Florence Regol

Joud Chataoui

Mark Coates

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net