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Found 7 papers, 1 papers with code
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds
Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studies or global normative approaches.
Understanding Rare Spurious Correlations in Neural Networks
Neural networks are known to use spurious correlations such as background information for classification.
Approximability of all Boolean CSPs with linear sketches
In this work we consider the approximability of $\textsf{Max-CSP}(f)$ in the context of sketching algorithms and completely characterize the approximability of all Boolean CSPs.
Computational Complexity Data Structures and Algorithms
A General Framework for Analyzing Stochastic Dynamics in Learning Algorithms
In this work, we present a streamlined three-step recipe to tackle the "chicken and egg" problem and give a general framework for analyzing stochastic dynamics in learning algorithms.
ODE-Inspired Analysis for the Biological Version of Oja's Rule in Solving Streaming PCA
In this work, we give the first convergence rate analysis for the biological version of Oja's rule in solving streaming PCA.
(Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs
Specifically, for every $\gamma>0$, we give a $n^{O(\log n)}$ time algorithm that given a pair of $\gamma$-correlated $G(n, p)$ graphs $G_0, G_1$ with average degree between $n^{\varepsilon}$ and $n^{1/153}$ for $\varepsilon = o(1)$, recovers the "ground truth" permutation $\pi\in S_n$ that matches the vertices of $G_0$ to the vertices of $G_n$ in the way that minimizes the number of mismatched edges.
On the Algorithmic Power of Spiking Neural Networks
Our main technical insight is a dual view of the SNN dynamics, under which SNN can be viewed as a new natural primal-dual algorithm for the l1 minimization problem.
