Clustering units in neural networks: upstream vs downstream information
Richard D Lange
David Rolnick
Konrad Paul Kording
It has been hypothesized that some form of"modular"structure in artificial neural networks should be useful for learning, compositionality, … (see more)and generalization. However, defining and quantifying modularity remains an open problem. We cast the problem of detecting functional modules into the problem of detecting clusters of similar-functioning units. This begs the question of what makes two units functionally similar. For this, we consider two broad families of methods: those that define similarity based on how units respond to structured variations in inputs ("upstream"), and those based on how variations in hidden unit activations affect outputs ("downstream"). We conduct an empirical study quantifying modularity of hidden layer representations of simple feedforward, fully connected networks, across a range of hyperparameters. For each model, we quantify pairwise associations between hidden units in each layer using a variety of both upstream and downstream measures, then cluster them by maximizing their"modularity score"using established tools from network science. We find two surprising results: first, dropout dramatically increased modularity, while other forms of weight regularization had more modest effects. Second, although we observe that there is usually good agreement about clusters within both upstream methods and downstream methods, there is little agreement about the cluster assignments across these two families of methods. This has important implications for representation-learning, as it suggests that finding modular representations that reflect structure in inputs (e.g. disentanglement) may be a distinct goal from learning modular representations that reflect structure in outputs (e.g. compositionality).
2022-06-13
TMLR (accepted)
doi.org
openreview.net
Studying the Practices of Deploying Machine Learning Projects on Docker
Moses Openja
Forough Majidi
Foutse Khomh
Bhagya Chembakottu
Heng Li
2022-06-13
The International Conference on Evaluation and Assessment in Software Engineering 2022 (published)
doi.org
arxiv.org
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
Samuel Sokota
Ryan D’orazio
J. Z. Kolter
Nicolas Loizou
Marc Lanctot
Ioannis Mitliagkas
Noam Brown
Christian Kroer
This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gra… (see more)dient algorithm. Our contribution is demonstrating the virtues of magnetic mirror descent as both an equilibrium solver and as an approach to reinforcement learning in two-player zero-sum games. These virtues include: 1) Being the first quantal response equilibria solver to achieve linear convergence for extensive-form games with first order feedback; 2) Being the first standard reinforcement learning algorithm to achieve empirically competitive results with CFR in tabular settings; 3) Achieving favorable performance in 3x3 Dark Hex and Phantom Tic-Tac-Toe as a self-play deep reinforcement learning algorithm.
2022-06-12
ArXiv (preprint)
doi.org
arxiv.org
The distribution, ecology and predicted habitat use of the Critically Endangered angelshark (Squatina squatina) in coastal waters of Wales and the central Irish Sea
Joanna Barker
Jake Davies
Monika Goralczyk
Surshti Patel
John O'Connor
Jim Evans
Jackson Wesley Evans
Rowland Sharp
Matthew Gollock
Fenella R. Wood
Frank Wood
James Rosindell
Charlie Bartlett
Brett J. Garner
Dafydd Jones
D. J. Jones
Declan Quigley
Ben Wray
Billy Wray
Abstract The angelshark (Squatina squatina) has the northernmost range of any angel shark species, but there is limited information on its d… (see more)istribution, habitat use and ecology at higher latitudes. To address this, Angel Shark Project: Wales gathered 2231 S. squatina records and 142 anecdotal resources from fishers, coastal communities and archives. These spanned the coastal waters of Wales and the central Irish Sea and were dated from 1812 to 2020, with 97.62% of records within 11.1 km (6 nm) of the coast. Commercial, recreational and charter boat fishers provided the majority of S. squatina records (97.18%), with significantly more sightings from three decades (1970s, 1980s and 1990s) and in the months of September, June, August and July (in descending order). The coastal area between Bardsey Island and Strumble Head had the most S. squatina records (n = 1279), with notable concentrations also found in Carmarthen Bay, Conwy Bay and the Outer Severn Estuary. Species distribution models (SDM) identified four environmental variables that had significant influence on S. squatina distribution, depth, chlorophyll‐a concentration, sea surface temperature (SST) and salinity, and these varied between the quarters (Q) of the year. SDM model outputs predicted a larger congruous area of suitable habitat in Q3 (3176 km2) compared to Q2 (2051 km2), with suitability along the three glacial moraines (Sarn Badrig, Sarn‐y‐Bwch and Sarn Cynfelyn) strongly presented. Comparison of modelled environmental variables at the location of S. squatina records for each Q identified reductions in depth and salinity, and increases in chlorophyll‐a and SST when comparing Q2 or Q3 with Q1 or Q4. This shift may suggest S. squatina are making seasonal movements to shallow coastal waters in Q2 and Q3. This is supported by 23 anecdotal resources and may be driven by reproductive behaviour, as there were 85 records of S. squatina individuals ≤60 cm in the dataset, inferred as recently born or juvenile life‐history stages. The results have helped fill significant evidence gaps identified in the Wales Angelshark Action Plan and immediate next research steps are suggested.
2022-06-11
Journal of Fish Biology (published)
doi.org
Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists
Ulrich Aivodji
Julien Ferry
Sébastien Gambs
Marie-José Huguet
Mohamed
Siala
2022-06-10
Integration of Constraint Programming, Artificial Intelligence, and Operations Research (published)
doi.org
On Neural Architecture Inductive Biases for Relational Tasks
Giancarlo Kerg
Sarthak Mittal
David Rolnick
Yoshua Bengio
Blake Richards
Guillaume Lajoie
Current deep learning approaches have shown good in-distribution generalization performance, but struggle with out-of-distribution generaliz… (see more)ation. This is especially true in the case of tasks involving abstract relations like recognizing rules in sequences, as we find in many intelligence tests. Recent work has explored how forcing relational representations to remain distinct from sensory representations, as it seems to be the case in the brain, can help artificial systems. Building on this work, we further explore and formalize the advantages afforded by 'partitioned' representations of relations and sensory details, and how this inductive bias can help recompose learned relational structure in newly encountered settings. We introduce a simple architecture based on similarity scores which we name Compositional Relational Network (CoRelNet). Using this model, we investigate a series of inductive biases that ensure abstract relations are learned and represented distinctly from sensory data, and explore their effects on out-of-distribution generalization for a series of relational psychophysics tasks. We find that simple architectural choices can outperform existing models in out-of-distribution generalization. Together, these results show that partitioning relational representations from other information streams may be a simple way to augment existing network architectures' robustness when performing out-of-distribution relational computations.
2022-06-09
ArXiv (preprint)
doi.org
arxiv.org
Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems
Devang Kulshreshtha
Muhammad Shayan
Robert Belfer
Siva Reddy
Iulian V. Serban
Ekaterina Kochmar
2022-06-08
ArXiv (preprint)
doi.org
arxiv.org
Interacting brains revisited: A cross‐brain network neuroscience perspective
Christian Gerloff
Kerstin Konrad
Danilo Bzdok
Christina Büsing
Vanessa Reindl
2022-06-06
Human Brain Mapping (published)
doi.org
Technologically-assisted communication attenuates inter-brain synchrony
Linoy Schwartz
Jonathan Levy
Yaara Endevelt-Shapira
Amir Djalovski
Olga Hayut
Guillaume Dumas
Ruth Pinkenson Feldman
2022-06-06
NeuroImage (published)
doi.org
Modeling electronic health record data using a knowledge-graph-embedded topic model
Yuesong Zou
Ahmad Pesaranghader
Aman Verma
David Buckeridge
Yue Li
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic wa… (see more)y. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations.
2022-06-03
ArXiv (preprint)
doi.org
arxiv.org
Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI
Agah Karakuzu
Labonny Biswas
Julien Cohen-Adad
Nikola Stikov
Purpose We developed an end-to-end workflow that starts with a vendor-neutral acquisition and tested the hypothesis that vendor-neutral sequ… (see more)ences decrease inter-vendor variability of T1, MTR and MTsat measurements. Methods We developed and deployed a vendor-neutral 3D spoiled gradient-echo (SPGR) sequence on three clinical scanners by two MRI vendors. We then acquired T1 maps on the ISMRM-NIST system phantom, as well as T1, MTR and MTsat maps in three healthy participants. We performed hierarchical shift function analysis in vivo to characterize the differences between scanners when the vendor-neutral sequence is used instead of commercial vendor implementations. Inter-vendor deviations were compared for statistical significance to test the hypothesis. Results In the phantom, the vendor-neutral sequence reduced inter-vendor differences from 8 - 19.4% to 0.2 - 5% with an overall accuracy improvement, reducing ground truth T1 deviations from 7 - 11% to 0.2 - 4%. In vivo we found that the variability between vendors is significantly reduced (p = 0.015) for all maps (T1, MTR and MTsat) using the vendor-neutral sequence. Conclusion We conclude that vendor-neutral workflows are feasible and compatible with clinical MRI scanners. The significant reduction of inter-vendor variability using vendor-neutral sequences has important implications for qMRI research and for the reliability of multicenter clinical trials.
2022-06-03
Magnetic Resonance in Medicine (published)
doi.org
Genetic correlates of phenotypic heterogeneity in autism
Varun Warrier
Xinhe Zhang
Patrick Reed
Alexandra Havdahl
Tyler M. Moore
Freddy Cliquet
Claire Leblond
Thomas Rolland
Anders Rosengren
Antonia San Jose Hannah Daisy Jessica Jessica Claire Bethany Eva Tony Declan Rosemary Jack Jessica Nicola Meng-Chuan Gwilym Amber Emily Hisham Julia Sara Ambrosino Sarai Yvonne Tabitha Miriam Alyssia Iris Maarten Anna Ver Loren Nico Sarah Larry Carsten Annika Daniel Ineke Yvette Maartje Elzbieta Elodie Kristiina Rouslan Guillaume Yang-Min Thomas Caceres
Antonia San Jose Hannah Daisy Jessica Jessica Claire Betha Caceres Hayward Crawley Faulkner Sabet Ellis Oakle
Antonia San José Cáceres
Hannah Hayward
Daisy Crawley
Jessica Faulkner
Jessica Sabet
Claire Ellis
Beth Oakley
Eva Loth
Tony Charman … (see 67 more)
Declan Murphy
Rosemary Holt
Jack Waldman
Jessica Upadhyay
Nicola Gunby
Meng-Chuan Lai
Gwilym Renouf
Amber N. V. Ruigrok
Emily Taylor
Hisham Ziauddeen
Julia Deakin
Sara Ambrosino di Bruttopilo
Sarai van Dijk
Yvonne Rijks
Tabitha Koops
Miriam Douma
Alyssia Spaan
Iris Selten
Maarten Steffers
Anna Ver Loren van Themaat
Nico Bast
Sarah Baumeister
Larry O’Dwyer
Carsten Bours
Annika Rausch
Daniel von Rhein
Ineke Cornelissen
Yvette de Bruin
Maartje Graauwmans
Elzbieta Kostrzewa
Elodie Cauvet
Kristiina Tammimies
Rouslan Sitnikow
Guillaume Dumas
Yang-Min Kim
Thomas Bourgeron
David M. Jonas Thomas Preben Bo Ole Merete Hougaard
David M. Hougaard
Jonas Bybjerg-Grauholm
Thomas Werge
Preben Bo Mortensen
Ole Mors
Merete Nordentoft
Dwaipayan Armandina Carrie Isabelle Tracey Paula Alex Graham J. Alexander E. P. Lidia V. Tal Madeline A. Deepak P. Jonathan Adhya
Dwaipayan Armandina Carrie Isabelle Tracey Paula Alex Graham Adhya Alamanza Allison Garvey Parsons Smith Tsompa
Dwaipayan Adhya
Armandina Alamanza
Carrie Allison
Isabelle Garvey
Tracey Parsons
Paula Smith
Alex Tsompanidis
Graham J. Burton
Alexander E. P. Heazell
Lidia V. Gabis
Tal Biron-Shental
Madeline A. Lancaster
Deepak P. Srivastava
Jonathan Mill
David H. Rowitch
Matthew E. Hurles
Daniel H. Geschwind
Anders D. Børglum
Elise B. Robinson
Jakob Grove
Hilary C. Martin
Simon Baron-Cohen
2022-06-02
Nature Genetics (published)
doi.org