Search Results for author: Mo Tiwari

Found 4 papers, 2 papers with code

GFlowNet Foundations

no code implementations17 Nov 2021 Yoshua Bengio, Tristan Deleu, Edward J. Hu, Salem Lahlou, Mo Tiwari, Emmanuel Bengio

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function.

Active Learning

Image Compression and Classification Using Qubits and Quantum Deep Learning

no code implementations8 Oct 2021 Ali Mohsen, Mo Tiwari

Our framework is able to classify images that are larger than previously possible, up to 16 x 16 for the MNIST dataset on a personal laptop, and obtains accuracy comparable to classical neural networks with the same number of learnable parameters.

Classification Image Classification +2

BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits

1 code implementation NeurIPS 2020 Mo Tiwari, Martin J. Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony

In these experiments, we observe that BanditPAM returns the same results as state-of-the-art PAM-like algorithms up to 4x faster while performing up to 200x fewer distance computations.

Multi-Armed Bandits

BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits

2 code implementations11 Jun 2020 Mo Tiwari, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony

Current state-of-the-art $k$-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size $n$ for each iteration, being prohibitively expensive for large datasets.

Multi-Armed Bandits

Cannot find the paper you are looking for? You can Submit a new open access paper.