#differentialalgorithms نتائج البحث
DiffeoMorph: Learning to Morph 3D Shapes Using Differentiable Agent-Based Simulations. arxiv.org/abs/2512.17129
...unless 1) you parameterize decisions stochastically, 2) there are many possible paths to a desired outcome, and 3) the loss function itself is smooth enough? Relevant: google-research.github.io/self-organisin…
google-research.github.io
Differentiable Logic Cellular Automata: From Game of Life to pattern generation with learned...
We use Differentiable Logic Gate Networks to create end-to-end differentiable, self-organizing discrete cellular automata powered by recurrent circuits, capable of playing the Game of Life as well as...
A golden learning resource for introduction to deep neural networks and differentiable programming. "Alice's Adventures in a Differentiable Wonderland" ✨ - Automatic differentiation, stochastic optimization, and activation functions in depth and related core concepts. -…
PhD Position in Probabilistic and Differential Algorithms 📍Utrecht, Netherlands Apply now: researchhires.com/position/b3eed… #PhD #ProbabilisticProgramming #DifferentialAlgorithms #UtrechtUniversity #Researchhires #MachineLearning #AI #STEMCareers
researchhires.com
ResearchHires | Find and Post Research Positions ( PhD, Postdoc and Masters)
ResearchHires is the leading platform for professors to easily post and manage PhD, doctorate, master's, and intern research positions across Canadian universities. Students can search and apply for...
The algorithm combines techniques from Bellman-Ford, a slower algorithm and Dijkstra’s to improve runtime Source: alphaxiv.org/abs/2504.17033
I’m excited to announce our call for papers for our #ICML2023 workshop “Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators”. differentiable.xyz If you are working on #differentiable things, consider submitting a 4-page paper.
I'm not the first to say this, but I'll repeat: an interesting thing about differential privacy is it needs theorems even in practice. You can implement heuristic algs that are fast on your data, but 'heuristic privacy' doesn't exist. A mechanism isn't private without a theorem.
Dijkstra’s algorithm computes the geodesic distance on a graph in O(n*log(n)) operations using a front propagation algorithm. en.wikipedia.org/wiki/Dijkstra%…
Laplace transforms can turn delay differential equations into algebraic equations.
The Laplacian operator is the divergence of the gradient.
Variation or parameters let’s you solve 90% of equations with 80% less work I would rather fail variation of parameters twice then start with undetermined coefficients also undetermined coefficients doesn’t work on all differential equations so there is that to consider as well
"There is no known analytical method to solve differential equations beyond the second order" Well, have you tried like, really hard ?
What happens when you use automatic differentiation and let your nonsmooth iterative algorithm goes to convergence? With J. Bolte & E. Pauwels, we show that under a contraction assumption, the derivatives of the algorithm converge linearly! Preprint: arxiv.org/abs/2206.00457 1/4
These are the steps you can take to understand a differential equation: 1) Exact solutions 2) Abstract analysis 3) Perturbation theory 4) Numerical simulation 5) Machine learning This numbered list somewhat reflects historical developments. Here's a physicist's view. 🧵(n=13)
Differentiable Vector Graphics Rasterization for Editing and Learning (SIGGRAPH Asia 2020) Nice work that allows backpropagation through an image rasterizer, so we can apply the goodies that work on pixel images to vector graphics people.csail.mit.edu/tzumao/diffvg/ github.com/BachiLi/diffvg
Materials for my class at PSL uni on "duality in machine learning". Topics covered include conjugate functions, smoothing techniques, Fenchel duality, Fenchel-Young losses and block dual coordinate ascent algorithms. Slides: mblondel.org/teaching/duali… Code: github.com/mblondel/teach…
Differentiable Digital Signal Processing (DDSP)! Fusing classic interpretable DSP with neural networks. ⌨️ Blog: magenta.tensorflow.org/ddsp 🎵 Examples: g.co/magenta/ddsp-e… ⏯ Colab: g.co/magenta/ddsp-d… 💻 Code: github.com/magenta/ddsp 📝 Paper: g.co/magenta/ddsp-p… 1/
Interested in a differentiable bipartite matching algorithm with theoretical convergence guarantee and <50 lines of Pytorch code? Please check our ICCV work: arxiv.org/abs/1909.12471 Code: github.com/ZENGXH/DMM_Net With Xiaohui, Li, Yuwen, @FidlerSanja and Raquel
New documentation about how differentiable programming works in Swift: • Differentiable Functions and Differentiation APIs: github.com/tensorflow/swi… • Differentiable Types: github.com/tensorflow/swi… With language integration, autodiff is just a compiler implementation detail.
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