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Found 9 papers, 0 papers with code
Fast Heavy Inner Product Identification Between Weights and Inputs in Neural Network Training
In this paper, we consider a heavy inner product identification problem, which generalizes the Light Bulb problem~(\cite{prr89}): Given two sets $A \subset \{-1,+1\}^d$ and $B \subset \{-1,+1\}^d$ with $|A|=|B| = n$, if there are exact $k$ pairs whose inner product passes a certain threshold, i. e., $\{(a_1, b_1), \cdots, (a_k, b_k)\} \subset A \times B$ such that $\forall i \in [k], \langle a_i, b_i \rangle \geq \rho \cdot d$, for a threshold $\rho \in (0, 1)$, the goal is to identify those $k$ heavy inner products.
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks
Automatically evaluating vision-language tasks is challenging, especially when it comes to reflecting human judgments due to limitations in accounting for fine-grained details.
Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?
A rising trend in theoretical deep learning is to understand why deep learning works through Neural Tangent Kernel (NTK) [jgh18], a kernel method that is equivalent to using gradient descent to train a multi-layer infinitely-wide neural network.
Online Adaptive Mahalanobis Distance Estimation
Mahalanobis metrics are widely used in machine learning in conjunction with methods like $k$-nearest neighbors, $k$-means clustering, and $k$-medians clustering.
Efficient SGD Neural Network Training via Sublinear Activated Neuron Identification
Deep learning has been widely used in many fields, but the model training process usually consumes massive computational resources and time.
Fast Submodular Function Maximization
We consider both the online and offline versions of the problem: in each iteration, the data set changes incrementally or is not changed, and a user can issue a query to maximize the function on a given subset of the data.
A General Algorithm for Solving Rank-one Matrix Sensing
In this paper, we relax that rank-$k$ assumption and solve a much more general matrix sensing problem.
Adaptive and Dynamic Multi-Resolution Hashing for Pairwise Summations
In this paper, we propose Adam-Hash: an adaptive and dynamic multi-resolution hashing data-structure for fast pairwise summation estimation.
A Sublinear Adversarial Training Algorithm
For a neural network of width $m$, $n$ input training data in $d$ dimension, it takes $\Omega(mnd)$ time cost per training iteration for the forward and backward computation.
