What is Next?

New Types of Deep Models

Physics Informed Neural Networks: Motivation

PINNs: Scenarios

Direct Application of Neural Networks

• Purely data-driven approaches $$\min \frac{1}{N}\sum_{i=1}^N \left(u_{NN}(x_i;\theta) - u_{true}(x_i)\right)^2$$

Physics Informed Neural Networks

$$m\frac{d^2u}{dx^2}+\mu\frac{du}{dx}+ku=0$$

• $$ \begin{aligned} \min & \frac{1}{N}\sum_{i=1}^N\left(u_{NN}(x_i;\theta)-u_{true}(x_i)\right)^2 \\ & + \frac{1}{M}\sum_{j=1}^M \left(\left[m\frac{d^2}{dx^2}+\mu\frac{d}{dx}+k\right]u_{NN}(x_k;\theta)\right) \end{aligned} $$

Physics Informed Neural Networks

• Raissi et al, ”Physics Informed Deep Learning: Data-driven Solutions of Nonlinear Partial Differential Equations.”, Arxiv 2017

• Krishnapriyan et al, ”Characterizing possible failure modes in physics-informed neural networks”, NeurIPS 2021

Fourier Neural Operators

• Li et al, ”Fourier Neural Operator for Partial Differential Equations”, ICLR 2021

Fourier Neural Operators

• Li et al, ”Fourier Neural Operator for Partial Differential Equations”, ICLR 2021

New Applications of Deep Learning

Deep Learning for Graphics: NVIDIA DLSS

Deep Learning for Graphics: NVIDIA DLSS

Deep Learning for Graphics: NVIDIA DLSS

Deep Learning for Graphics: NeRF

• Mildenhall et al, ”NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis”, ECCV 2020

Deep Learning for Scientific Applications

Deep Learning for Scientific Applications

Lu et al 2020
Galaxy Classficiation

Dielman et al 2014

Whale Recognition

Kaggle Challenge Bear Recognition

BearID Project

Weather Forecasting with FNO

Weather Forecasting with GNNs

Deep Learning for Science: Protein Folding

• Input: 1D sequence of amino acids
• Output: 3D protein structure

Deep Learning for Science: Protein Folding

Deep Learning for Science: Protein Folding

Deep Learning for Mathematics

• Convert mathematical expressions into graphs, process them with graph neural networks.

• Applications: Theorem proving, symbolic integration

• Wang et al, "Premise selection for Theorem Proving by Deep Graph Embedding", NeurIPS 2017
• Kaliszyk et al, "Reinforcement Learning of Theorem Proving", NeurIPS 2018
• Wang et al, "Deep Learning for Symbolic Mathematics", Arxiv 2019

AutoML: Neural Architecture Search

Early DNN Architecture Development

• Primarily driven by skilled practitioners and elaborated design.
• a.k.a "Graduate Student Design"

Automating DNN Design

The Promise of NAS

Problems with Deep Learning

Empirical Mystery: Good Subnetworks

Empirical Mystery: Good Subnetworks

• We do not understand how to train and initialize deep networks, and what training actually does.

• Ramanujan et al "What's hidden in a randomly weighted neural network?," arxiv 2019

Empirical Mystery: Generalization

• What we expect from classical statistical learning theory:
• Why don't deep neural networks overfit?
• "Double Descent" for deep networks does not match theory.

• Belkin et al "Reconciling modeern machine learning practice and the bias-variance trade-off," PNAS 2019

Deep Learning Does Not "Understand" the World

Language Models Lack Common Sense

• Input: I was born in 1950. In the year 2025 my age will be 35.
• Response from GPT-2: That was only a few years ago. Most things in life just continue to improve.
• Input: I see a black dog and a brown horse. The bigger animal's color is
• Response from GPT-2: black, and the smaller is brown.
• One of my parents is a doctor and the other is a professor. My father is a professor. My mother is
• Response from GPT-2: a social worker. They're super smart people.

"The Elephant in the Room"

• Rosenfeld et al "The Elephant in the Room," arxiv 2018

"The Elephant in the Room"

• Rosenfeld et al "The Elephant in the Room," arxiv 2018

Causality

• Wang and Boddeti, "Do learned representations respect causal relationships?," CVPR 2022