Search Results for author:
Found 12 papers, 4 papers with code
Results of the Big ANN: NeurIPS'23 competition
The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads.
Bridging Dense and Sparse Maximum Inner Product Search
Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top-$k$ retrieval in Information Retrieval.
An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors
To achieve optimal memory footprint and query latency, they rely on the near stationarity of documents and on laws governing natural languages.
Even Simpler Deterministic Matrix Sketching
This paper provides a one-line proof of Frequent Directions (FD) for sketching streams of matrices.
Streaming Quantiles Algorithms with Small Space and Update Time
Approximating quantiles and distributions over streaming data has been studied for roughly two decades now.
Asymmetric Random Projections
In many applications the data set to be projected is given to us in advance, yet the current RP techniques do not make use of information about the data.
Discrepancy, Coresets, and Sketches in Machine Learning
We provide general techniques for bounding the class discrepancy of machine learning problems.
ProxQuant: Quantized Neural Networks via Proximal Operators
To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights.
Optimal Quantile Approximation in Streams
One of our contributions is a novel representation and modification of the widely used merge-and-reduce construction.
Data Structures and Algorithms
Frequent Directions : Simple and Deterministic Matrix Sketching
It performed $O(d \times \ell)$ operations per row and maintains a sketch matrix $B \in R^{\ell \times d}$ such that for any $k < \ell$ $\|A^TA - B^TB \|_2 \leq \|A - A_k\|_F^2 / (\ell-k)$ and $\|A - \pi_{B_k}(A)\|_F^2 \leq \big(1 + \frac{k}{\ell-k}\big) \|A-A_k\|_F^2 $ .
Data Structures and Algorithms 68W40 (Primary)
An Algorithm for Online K-Means Clustering
We also show that, experimentally, it is not much worse than k-means++ while operating in a strictly more constrained computational model.
Near-Optimal Entrywise Sampling for Data Matrices
We consider the problem of selecting non-zero entries of a matrix $A$ in order to produce a sparse sketch of it, $B$, that minimizes $\|A-B\|_2$.
