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