Decision-focused learning: Foundations, state of the art, benchmark and future opportunities
Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …
(ML) and constrained optimization to enhance decision quality by training ML models in an …
A survey of contextual optimization methods for decision-making under uncertainty
Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …
learning (ML) community in combining prediction algorithms and optimization techniques to …
Raft: Recurrent all-pairs field transforms for optical flow
Z Teed, J Deng - Computer Vision–ECCV 2020: 16th European …, 2020 - Springer
Abstract We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network
architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D …
architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D …
On neural differential equations
P Kidger - arxiv preprint arxiv:2202.02435, 2022 - arxiv.org
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
Safe control with learned certificates: A survey of neural lyapunov, barrier, and contraction methods for robotics and control
Learning-enabled control systems have demonstrated impressive empirical performance on
challenging control problems in robotics, but this performance comes at the cost of reduced …
challenging control problems in robotics, but this performance comes at the cost of reduced …
Efficient and modular implicit differentiation
Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …
complex computations by composing elementary ones in creativeways and removes the …
Theseus: A library for differentiable nonlinear optimization
We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …
Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis
Deep learning has recently emerged as a disruptive technology to solve challenging radio
resource management problems in wireless networks. However, the neural network …
resource management problems in wireless networks. However, the neural network …
Model-based deep learning: On the intersection of deep learning and optimization
Decision making algorithms are used in a multitude of different applications. Conventional
approaches for designing decision algorithms employ principled and simplified modelling …
approaches for designing decision algorithms employ principled and simplified modelling …
DC3: A learning method for optimization with hard constraints
Large optimization problems with hard constraints arise in many settings, yet classical
solvers are often prohibitively slow, motivating the use of deep networks as cheap" …
solvers are often prohibitively slow, motivating the use of deep networks as cheap" …