Accepted Papers (2024)
Differentiable Short-Time Fourier Transform: A Time-Frequency Layer with Learnable Parameters, Maxime Leiber, yosra marnissi, Axel Barrau
Differentiable Soft Min-Max Loss to Restrict Weight Range for Model Quantization, Arnav Kundu, Chungkuk Yoo, Minsik Cho, Saurabh Adya
Learning to Design Data-structures: A Case Study of Nearest Neighbor Search, Omar Salemohamed, Vatsal Sharan, Shivam Garg, Laurent Charlin, Gregory Valiant
Transforming a Non-Differentiable Rasterizer into a Differentiable One with Stochastic Gradient Estimation, Thomas Deliot, Eric Heitz, Laurent Belcour
Relaxing Graph Transformers for Adversarial Attacks, Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann
You Shall Pass: Dealing with the Zero-Gradient Problem in Predict and Optimize for Convex Optimization, Grigorii Veviurko, Wendelin Boehmer, Mathijs de Weerdt
PICT: Adaptive GPU Accelerated Differentiable Fluid Simulation for Machine Learning, Erik Franz, Nils Thuerey
Revisiting Score Function Estimators for $k$-Subset Sampling, Klas Wijk, Ricardo Vinuesa Motilva, Hossein Azizpour
Differentiable Mapper for Topological Optimization of Data Representation, Ziyad Oulhaj, Mathieu Carrière, Bertrand Michel
Differentiable Iterated Function Systems, Cory Braker Scott
Differentiable Weighted Automata, Anand Balakrishnan, Jyotirmoy V. Deshmukh
Learning Set Functions with Implicit Differentiation, Gözde Özcan, Chengzhi Shi, Stratis Ioannidis
SA-DQAS: Self-attention Enhanced Differentiable Quantum Architecture Search, Yize Sun, Jiarui Liu, Zixin Wu, Zifeng Ding, Yunpu Ma, Thomas Seidl, Volker Tresp
Differentiable Local Intrinsic Dimension Estimation with Diffusion Models, Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem
Using gradients to check sensitivity of MCMC-based analyses to removing data, Tin D. Nguyen, Ryan James Giordano, Rachael Meager, Tamara Broderick
A Differentiable Approach to Multi-scale Brain Modeling, Chaoming Wang, Muyang Lyu, Tianqiu Zhang, Sichao He, Si Wu
End-to-end Differentiable Model of Robot-terrain Interactions, Ruslan Agishev, Vladimír Kubelka, Martin Pecka, Tomas Svoboda, Karel Zimmermann
Differentiable Cost-Parameterized Monge Map Estimators, Samuel Howard, George Deligiannidis, Patrick Rebeschini, James Thornton
Symbolic Autoencoding for Self-Supervised Sequence Learning, Mohammad Hossein Amani, Nicolas Baldwin, Amin Mansouri, Martin Josifoski, Maxime Peyrard, Robert West
Generalizing Convolution to Point Clouds, Davide Bacciu, Francesco Landolfi
Differentiable Cluster Graph Neural Network, Yanfei Dong, Mohammed Haroon Dupty, Lambert Deng, Zhuanghua Liu, Yong Liang Goh, Wee Sun Lee
CGMTorch: A Framework for Gradient-based Design of Computational Granular Metamaterials, Atoosa Parsa, Corey OHern, Rebecca Kramer-Bottiglio, Josh Bongard
(Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition Number, Rossen Nenov, Daniel Haider, Peter Balazs
Differentiable Approximations of Fair OWA Optimization, My H Dinh, James Kotary, Ferdinando Fioretto
Implicit Diffusion: Efficient Optimization through Stochastic Sampling, Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet
BMapEst: Estimation of Brain Tissue Probability Maps using a Differentiable MRI Simulator, Utkarsh Gupta, Emmanouil Nikolakakis, Moritz Zaiss, Razvan Marinescu
Stable Differentiable Causal Discovery, Achille Nazaret, Justin Hong, Elham Azizi, David Blei
Differentiable Wireless Simulation with Geometric Transformers, Thomas Hehn, Markus Peschl, Tribhuvanesh Orekondy, Arash Behboodi, Johann Brehmer
MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation, Alexandre Hayderi, Amin Saberi, Ellen Vitercik, Anders Wikum
Enhancing Concept-based Learning with Logic, Deepika Vemuri, Gautham Bellamkonda, Vineeth N. Balasubramanian
How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent, Konstantin Riedl, Timo Klock, Carina Geldhauser, Massimo Fornasier
DiffFit: Differentiable Fitting of Molecule Structures to a Cryo-EM Map, Deng Luo, Zainab Alsuwaykit, Dawar Khan, Ondrej Strnad, Tobias Isenberg, Ivan Viola
$\bf{\Phi}_\textrm{Flow}$: Differentiable Simulations for Machine Learning, Philipp Holl, Nils Thuerey
Heterogeneous Federated Zeroth-Order Optimization using Gradient Surrogates, Yao Shu, Xiaoqiang Lin, Zhongxiang Dai, Bryan Kian Hsiang Low
A framework for differentiable Supervised Graph Prediction, Paul KRZAKALA, Junjie Yang, Rémi Flamary, Florence d’Alché-Buc, Charlotte Laclau, Matthieu Labeau
A Differentiable Topological Notion of Local Maxima for Keypoint Detection, Giovanni Barbarani, Francesco Vaccarino, Gabriele Trivigno, Marco Guerra, Gabriele Berton, Carlo Masone
Parallelising Differentiable Algorithms Removes the Scalar Bottleneck: A Case Study, Euan Ong, Ferenc Huszár, Pietro Lio, Petar Veličković
BPNAS: Bayesian Progressive Neural Architecture Search, Hyunwoong Chang, Anirban Samaddar, Sandeep Madireddy
Analyzing and Improving Surrogate Gradient Training in Binary Neural Networks Using Dynamical Systems Theory, Rainer Engelken, Larry Abbott
Structure- and Function-Aware Substitution Matrices via Differentiable Graph Matching, Paolo Pellizzoni, Carlos Oliver, Karsten Borgwardt
Energy-based Hopfield Boosting for Out-of-Distribution Detection, Claus Hofmann, Simon Lucas Schmid, Bernhard Lehner, Daniel Klotz, Sepp Hochreiter
BiPer: Binary Neural Networks using a Periodic Function, Edwin Vargas, Claudia V. Correa, Carlos Hinojosa, Henry Arguello
Contact
Contact the organizers: mail@differentiable.xyz