7 citations · 10 across the 6 of their papers we have counts for
7 papers
MLPerf Mobile Inference Benchmark
Vijay Janapa Reddi, David Kanter, Peter Mattson +24
This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips…
Hashing with Binary Matrix Pursuit
Fatih Cakir, Kun He, Stan Sclaroff
We propose theoretical and empirical improvements for two-stage hashing methods. We first provide a theoretical analysis on the quality of the binary codes and show that, under mil…
Hashing with Mutual Information
Fatih Cakir, Kun He, Sarah Adel Bargal +1
Binary vector embeddings enable fast nearest neighbor retrieval in large databases of high-dimensional objects, and play an important role in many practical applications, such as i…
Hashing as Tie-Aware Learning to Rank
Kun He, Fatih Cakir, Sarah Adel Bargal +1
Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at dire…
MIHash: Online Hashing with Mutual Information
Fatih Cakir, Kun He, Sarah Adel Bargal +1
Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by lear…
Online Supervised Hashing for Ever-Growing Datasets
Fatih Cakir, Sarah Adel Bargal, Stan Sclaroff
Supervised hashing methods are widely-used for nearest neighbor search in computer vision applications. Most state-of-the-art supervised hashing approaches employ batch-learners. U…
Visual Word Selection without Re-Coding and Re-Pooling
Fatih Cakir, Stan Sclaroff
The Bag-of-Words (BoW) representation is widely used in computer vision. The size of the codebook impacts the time and space complexity of the applications that use BoW. Thus, give…