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Workshop
Interpretable Inductive Biases and Physically Structured Learning
Michael Lutter · Alexander Terenin · Shirley Ho · Lei Wang

Sat Dec 12 06:30 AM -- 02:30 PM (PST) @ None
Event URL: https://inductive-biases.github.io/ »

Over the last decade, deep networks have propelled machine learning to accomplish tasks previously considered far out of reach, human-level performance in image classification and game-playing. However, research has also shown that the deep networks are often brittle to distributional shifts in data: it has been shown that human-imperceptible changes can lead to absurd predictions. In many application areas, including physics, robotics, social sciences and life sciences, this motivates the need for robustness and interpretability, so that deep networks can be trusted in practical applications. Interpretable and robust models can be constructed by incorporating prior knowledge within the model or learning process as an inductive bias, thereby regularizing the model, avoiding overfitting, and making the model easier to understand for scientists who are non-machine-learning experts. Already in the last few years researchers from different fields have proposed various combinations of domain knowledge and machine learning and successfully applied these techniques to various applications.

Sat 6:30 a.m. - 6:35 a.m.
Introduction (Live)
Sat 6:35 a.m. - 6:50 a.m.
Thomas Pierrot - Learning Compositional Neural Programs for Continuous Control (Contributed Talk)   
Thomas PIERROT
Sat 6:50 a.m. - 7:10 a.m.
Jessica Hamrick - Structured Computation and Representation in Deep Reinforcement Learning (Invited Talk)   
Jessica Hamrick
Sat 7:10 a.m. - 7:25 a.m.
Manu Kalia - Deep learning of normal form autoencoders for universal, parameter-dependent dynamics (Contributed Talk)   
Manu Kalia
Sat 7:25 a.m. - 7:50 a.m.
Rose Yu - Physics-Guided AI for Learning Spatiotemporal Dynamics (Invited Talk)   
Rose Yu
Sat 7:50 a.m. - 8:05 a.m.
Ferran Alet - Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time (Contributed Talk)
Ferran Alet
Sat 8:05 a.m. - 9:00 a.m.
Poster Session 1 (Poster Session)
Sat 9:00 a.m. - 9:25 a.m.
Frank Noé - PauliNet: Deep Neural Network Solution of the Electronic Schrödinger Equation (Invited Talk)   
Frank Noe
Sat 9:25 a.m. - 9:40 a.m.
Kimberly Stachenfeld - Graph Networks with Spectral Message Passing (Contributed Talk)   
Kim Stachenfeld
Sat 9:40 a.m. - 10:10 a.m.
Franziska Meier - Inductive Biases for Models and Learning-to-Learn (Invited Talk)   
Franziska Meier
Sat 10:10 a.m. - 10:25 a.m.
Rui Wang - Shapley Explanation Networks (Contributed Talk)   
Rui Wang
Sat 10:25 a.m. - 10:55 a.m.
Jeanette Bohg - One the Role of Hierarchies for Learning Manipulation Skills (Invited Talk)   
Christin Jeannette Bohg
Sat 11:00 a.m. - 12:00 p.m.
Panel Discussion
Sat 12:00 p.m. - 1:00 p.m.
Poster Session 2 (Posters)
Sat 12:00 p.m. - 1:00 p.m.
1 - Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs (Poster)   
Giorgio Giannone
Sat 12:00 p.m. - 1:00 p.m.
2 - Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties (Poster)   
Benjamin K Miller
Sat 12:00 p.m. - 1:00 p.m.
3 - Improving the trustworthiness of image classification models by utilizing bounding-box annotations (Poster)
Dharma R KC
Sat 12:00 p.m. - 1:00 p.m.
4 - Physics-informed Generative Adversarial Networks for Sequence Generation with Limited Data (Poster)   
Chacha Chen
Sat 12:00 p.m. - 1:00 p.m.
5 - On the Structure of Cyclic Linear Disentangled Representations (Poster)   
Matthew Painter
Sat 12:00 p.m. - 1:00 p.m.
6 - Interpretable Models for Granger Causality Using Self-explaining Neural Networks (Poster)   
Ričards Marcinkevičs
Sat 12:00 p.m. - 1:00 p.m.
7 - A Symmetric and Object-Centric World Model for Stochastic Environments (Poster)
Patrick Emami
Sat 12:00 p.m. - 1:00 p.m.
8 - Individuality in the hive - Learning to embed lifetime social behavior of honey bees (Poster)   
Benjamin Wild
Sat 12:00 p.m. - 1:00 p.m.
9 - Thermodynamic Consistent Neural Networks for Learning Material Interfacial Mechanics (Poster)   
Jiaxin Zhang
Sat 12:00 p.m. - 1:00 p.m.
10 - A Trainable Optimal Transport Embedding for Feature Aggregation (Poster)   
Grégoire Mialon
Sat 12:00 p.m. - 1:00 p.m.
11 - A novel approach for semiconductor etching process with inductive biases (Poster)   
Sanghoon Myung
Sat 12:00 p.m. - 1:00 p.m.
12 - Physics-aware, data-driven discovery of slow and stable coarse-grained dynamics for high-dimensional multiscale systems (Poster)   
Sebastian Kaltenbach
Sat 12:00 p.m. - 1:00 p.m.
12 - IV-Posterior: Inverse Value Estimation forInterpretable Policy Certificates (Poster)   
Tatiana López-Guevara
Sat 12:00 p.m. - 1:00 p.m.
13 - Gradient-based Optimization for Multi-resource Spatial Coverage (Poster)   
Nitin Kamra
Sat 12:00 p.m. - 1:00 p.m.
14 - Learning Dynamical Systems Requires Rethinking Generalization (Poster)   
Rui Wang
Sat 12:00 p.m. - 1:00 p.m.
15 - Lie Algebra Convolutional Networks with Automatic Symmetry Extraction (Poster)   
Nima Dehmamy
Sat 12:00 p.m. - 1:00 p.m.
16 - An Image is Worth 16 × 16 Tokens: Visual Priors for Efficient Image Synthesis with Transformers (Poster)   
Robin Rombach
Sat 12:00 p.m. - 1:00 p.m.
17 - Uncovering How Neural Network Representations Vary with Width and Depth (Poster)   
Thao Nguyen
Sat 12:00 p.m. - 1:00 p.m.
18 - Simulating Surface Wave Dynamics with Convolutional Networks (Poster)   
Sat 12:00 p.m. - 1:00 p.m.
19 - Choice of Representation Matters for Adversarial Robustness (Poster)   
Amartya Sanyal
Sat 12:00 p.m. - 1:00 p.m.
20 -SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency (Poster)   
Sameer Dharur
Sat 12:00 p.m. - 1:00 p.m.
21 - Solving Physics Puzzles by Reasoning about Paths (Poster)   
Augustin Harter
Sat 12:00 p.m. - 1:00 p.m.
22 - Modelling Advertising Awareness, an Interpretable and Differentiable Approach (Poster)   
Luz Blaz
Sat 12:00 p.m. - 1:00 p.m.
23 - Constraining neural networks output by an interpolating loss function with region priors (Poster)   
Hannes Bergkvist
Sat 12:00 p.m. - 1:00 p.m.
24 - Deep Context-Aware Novelty Detection (Poster)   
Ellen Rushe
Sat 12:00 p.m. - 1:00 p.m.
25 - Complex Skill Acquisition through Simple Skill Imitation Learning (Poster)
Pranay Pasula
Sat 12:00 p.m. - 1:00 p.m.
26 - Is the Surrogate Model Interpretable? (Poster)   
Sangwon Kim
Sat 1:00 p.m. - 1:15 p.m.
Liwei Chen - Deep Learning Surrogates for Computational Fluid Dynamics (Contributed Talk)   
Nils Thuerey
Sat 1:15 p.m. - 2:15 p.m.
Maziar Raissi - Hidden Physics Models (Invited Talk)   
Maziar Raissi
Sat 2:15 p.m. - 2:30 p.m.
Closing Remarks (Live)

Author Information

Michael Lutter (TU Darmstadt)
Alexander Terenin (Imperial College London)
Shirley Ho (Flatiron institute/ New York University/ Carnegie Mellon)
Lei Wang (IOP, CAS)

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