Search Results for author: Amr Ahmed

Found 24 papers, 2 papers with code

When in Doubt, Summon the Titans: Efficient Inference with Large Models

no code implementations19 Oct 2021 Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar

In a nutshell, we use the large teacher models to guide the lightweight student models to only make correct predictions on a subset of "easy" examples; for the "hard" examples, we fall-back to the teacher.

Image Classification

When in Doubt, Summon the Titans: A Framework for Efficient Inference with Large Models

no code implementations29 Sep 2021 Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar

In a nutshell, we use the large teacher models to guide the lightweight student models to only make correct predictions on a subset of "easy" examples; for the "hard" examples, we fall-back to the teacher.

Image Classification

Hierarchically Regularized Deep Forecasting

no code implementations14 Jun 2021 Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das

Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy.

Time Series

Non-Stationary Latent Bandits

no code implementations1 Dec 2020 Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Mohammad Ghavamzadeh, Craig Boutilier

The key idea is to frame this problem as a latent bandit, where the prototypical models of user behavior are learned offline and the latent state of the user is inferred online from its interactions with the models.

Frame online learning +1

Scalable Hierarchical Agglomerative Clustering

2 code implementations22 Oct 2020 Nicholas Monath, Avinava Dubey, Guru Guruganesh, Manzil Zaheer, Amr Ahmed, Andrew McCallum, Gokhan Mergen, Marc Najork, Mert Terzihan, Bryon Tjanaka, YuAn Wang, Yuchen Wu

The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.

2D Human Pose Estimation

Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

no code implementations15 Sep 2020 Xinyuan Zhang, Ruiyi Zhang, Manzil Zaheer, Amr Ahmed

High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task.

Abstractive Dialogue Summarization dialogue summary +1

Non-Stationary Off-Policy Optimization

no code implementations15 Jun 2020 Joey Hong, Branislav Kveton, Manzil Zaheer, Yin-Lam Chow, Amr Ahmed

This approach is practical and analyzable, and we provide guarantees on both the quality of off-policy optimization and the regret during online deployment.

Multi-Armed Bandits

Latent Bandits Revisited

no code implementations NeurIPS 2020 Joey Hong, Branislav Kveton, Manzil Zaheer, Yin-Lam Chow, Amr Ahmed, Craig Boutilier

A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state.

Recommendation Systems

Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies

no code implementations ICLR 2021 Paul Pu Liang, Manzil Zaheer, Yu-An Wang, Amr Ahmed

In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix.

Language Modelling Text Classification

Anchor & Transform: Learning Sparse Representations of Discrete Objects

no code implementations25 Sep 2019 Paul Pu Liang, Manzil Zaheer, YuAn Wang, Amr Ahmed

Learning continuous representations of discrete objects such as text, users, and items lies at the heart of many applications including text and user modeling.

Language Modelling Text Classification

Canopy --- Fast Sampling with Cover Trees

no code implementations ICML 2017 Manzil Zaheer, Satwik Kottur, Amr Ahmed, José Moura, Alex Smola

In this work, we propose Canopy, a sampler based on Cover Trees that is exact, has guaranteed runtime logarithmic in the number of atoms, and is provably polynomial in the inherent dimensionality of the underlying parameter space.

Latent LSTM Allocation: Joint clustering and non-linear dynamic modeling of sequence data

no code implementations ICML 2017 Manzil Zaheer, Amr Ahmed, Alexander J. Smola

Recurrent neural networks, such as long-short term memory (LSTM) networks, are powerful tools for modeling sequential data like user browsing history (Tan et al., 2016; Korpusik et al., 2016) or natural language text (Mikolov et al., 2010).

Recurrent Recommender Networks

no code implementations WSDM 2017 Chao-yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, How Jing

Recommender systems traditionally assume that user profiles and movie attributes are static.

Recommendation Systems

Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering

no code implementations6 Dec 2015 Chao-yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola

With this novel technique we propose a new Bayesian model for joint collaborative filtering of ratings and text reviews through a sum of simple co-clusterings.

Collaborative Filtering

High Performance Latent Variable Models

no code implementations21 Oct 2015 Aaron Q. Li, Amr Ahmed, Mu Li, Vanja Josifovski

Latent variable models have accumulated a considerable amount of interest from the industry and academia for their versatility in a wide range of applications.

ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly

no code implementations31 Dec 2014 Alex Beutel, Amr Ahmed, Alexander J. Smola

Matrix completion and approximation are popular tools to capture a user's preferences for recommendation and to approximate missing data.

Decision Making Matrix Completion

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