ICML 2024PastOther
ICML 2024 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators
Differentiable Almost Everything
- Submission deadline
- Jun 10, 2024, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (43)
Fetched from OpenReview (v2) on 2026-06-10.
(Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition Number
$\bf{\Phi}_\textrm{Flow}$: Differentiable Simulations for Machine Learning
A Differentiable Approach to Multi-scale Brain Modeling
A Differentiable Topological Notion of Local Maxima for Keypoint Detection
A framework for differentiable Supervised Graph Prediction
Analyzing and Improving Surrogate Gradient Training in Binary Neural Networks Using Dynamical Systems Theory
BiPer: Binary Neural Networks using a Periodic Function
BMapEst: Estimation of Brain Tissue Probability Maps using a Differentiable MRI Simulator
BPNAS: Bayesian Progressive Neural Architecture Search
CGMTorch: A Framework for Gradient-based Design of Computational Granular Metamaterials
Decoupled Differentiable Neural Architecture Search: Memory-Efficient Differentiable NAS via Disentangled Search Space
Differentiable Approximations of Fair OWA Optimization
Differentiable Cluster Graph Neural Network
Differentiable Cost-Parameterized Monge Map Estimators
Differentiable Iterated Function Systems
Differentiable Local Intrinsic Dimension Estimation with Diffusion Models
Differentiable Mapper for Topological Optimization of Data Representation
Differentiable Short-Time Fourier Transform: A Time-Frequency Layer with Learnable Parameters
Differentiable Soft Min-Max Loss to Restrict Weight Range for Model Quantization
Differentiable Weighted Automata
Differentiable Wireless Simulation with Geometric Transformers
DiffFit: Differentiable Fitting of Molecule Structures to a Cryo-EM Map
End-to-end Differentiable Model of Robot-terrain Interactions
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Enhancing Concept-based Learning with Logic
Generalizing Convolution to Point Clouds
Heterogeneous Federated Zeroth-Order Optimization using Gradient Surrogates
How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
Learning Set Functions with Implicit Differentiation
Learning to Design Data-structures: A Case Study of Nearest Neighbor Search
MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
Parallelising Differentiable Algorithms Removes the Scalar Bottleneck: A Case Study
PICT: Adaptive GPU Accelerated Differentiable Fluid Simulation for Machine Learning
Relaxing Graph Transformers for Adversarial Attacks
Revisiting Score Function Estimators for $k$-Subset Sampling
SA-DQAS: Self-attention Enhanced Differentiable Quantum Architecture Search
Stable Differentiable Causal Discovery
Structure- and Function-Aware Substitution Matrices via Differentiable Graph Matching
Symbolic Autoencoding for Self-Supervised Sequence Learning
Transforming a Non-Differentiable Rasterizer into a Differentiable One with Stochastic Gradient Estimation
Using gradients to check sensitivity of MCMC-based analyses to removing data
You Shall Pass: Dealing with the Zero-Gradient Problem in Predict and Optimize for Convex Optimization