Search Results for author:
Found 28 papers, 5 papers with code
Big Bird: Transformers for Longer Sequences
To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
Ranked #1 on
Text Classification
on Arxiv HEP-TH citation graph
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
In the online setting, where the algorithm has access to a single instance at a time, estimating the group fairness objective requires additional storage and significantly more computation (e. g., forward/backward passes) than the task-specific objective at every time step.
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Distributed representations provide a vector space that captures meaningful relationships between data instances.
Incremental Extractive Opinion Summarization Using Cover Trees
In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time.
State Space LSTM Models with Particle MCMC Inference
Long Short-Term Memory (LSTM) is one of the most powerful sequence models.
Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering
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.
High Performance Latent Variable Models
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
Matrix completion and approximation are popular tools to capture a user's preferences for recommendation and to approximate missing data.
FastEx: Hash Clustering with Exponential Families
Clustering is a key component in data analysis toolbox.
Latent LSTM Allocation: Joint clustering and non-linear dynamic modeling of sequence data
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).
Canopy --- Fast Sampling with Cover Trees
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.
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies
In this paper, we design a simple and efficient embedding algorithm that learns a small set of anchor embeddings and a sparse transformation matrix.
Non-Stationary Off-Policy Optimization
This approach is practical and analyzable, and we provide guarantees on both the quality of off-policy optimization and the regret during online deployment.
Latent Bandits Revisited
A latent bandit problem is one in which the learning agent knows the arm reward distributions conditioned on an unknown discrete latent state.
Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes
High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task.
Non-Stationary Latent Bandits
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.
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
To democratize access to machine learning systems, it is essential to automate the tuning.
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
A Review of Machine Learning Techniques for Applied Eye Fundus and Tongue Digital Image Processing with Diabetes Management System
Diabetes is a global epidemic and it is increasing at an alarming rate.
Exact and Approximate Hierarchical Clustering Using A*
In those cases, hierarchical clustering can be seen as a combinatorial optimization problem.
Hierarchically Regularized Deep Forecasting
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.
Recurrent Recommender Networks
Recommender systems traditionally assume that user profiles and movie attributes are static.
When in Doubt, Summon the Titans: A Framework for Efficient Inference with Large Models
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.
When in Doubt, Summon the Titans: Efficient Inference with Large Models
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.
Anchor & Transform: Learning Sparse Representations of Discrete Objects
Learning continuous representations of discrete objects such as text, users, and items lies at the heart of many applications including text and user modeling.
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
Unsupervised Opinion Summarization Using Approximate Geodesics
We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism.
