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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

Decoupled Differentiable Neural Architecture Search: Memory-Efficient Differentiable NAS via Disentangled Search Space, Libin Hou

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