Search Results for author:
Found 26 papers, 13 papers with code
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.
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention
We present Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT): a new paradigm for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-robot deployment.
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Distributed representations provide a vector space that captures meaningful relationships between data instances.
Scalable Neural Network Kernels
We introduce the concept of scalable neural network kernels (SNNKs), the replacements of regular feedforward layers (FFLs), capable of approximating the latter, but with favorable computational properties.
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.
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
Efficient Graph Field Integrators Meet Point Clouds
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds.
FAVOR#: Sharp Attention Kernel Approximations via New Classes of Positive Random Features
The problem of efficient approximation of a linear operator induced by the Gaussian or softmax kernel is often addressed using random features (RFs) which yield an unbiased approximation of the operator's result.
Karyotype AI for Precision Oncology
These individual chromosomes were used to train and assess deep learning models for classifying the 24 human chromosomes and identifying chromosomal aberrations.
A Fourier Approach to Mixture Learning
Regev and Vijayaraghavan (2017) showed that with $\Delta = \Omega(\sqrt{\log k})$ separation, the means can be learned using $\mathrm{poly}(k, d)$ samples, whereas super-polynomially many samples are required if $\Delta = o(\sqrt{\log k})$ and $d = \Omega(\log k)$.
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.
Chefs' Random Tables: Non-Trigonometric Random Features
We introduce chefs' random tables (CRTs), a new class of non-trigonometric random features (RFs) to approximate Gaussian and softmax kernels.
On Learning the Transformer Kernel
In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for learning the kernel function in Transformers.
Exact and Approximate Hierarchical Clustering Using A*
In those cases, hierarchical clustering can be seen as a combinatorial optimization problem.
Scalable Hierarchical Agglomerative Clustering
The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability.
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
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow.
Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.
Transformation Autoregressive Networks
Further, through a comprehensive study over both real world and synthetic data, we show for that jointly leveraging transformations of variables and autoregressive conditional models, results in a considerable improvement in performance.
Ranked #1 on
Density Estimation
on BSDS300
The Intriguing Properties of Model Explanations
Linear approximations to the decision boundary of a complex model have become one of the most popular tools for interpreting predictions.
Personalized Survival Prediction with Contextual Explanation Networks
Accurate and transparent prediction of cancer survival times on the level of individual patients can inform and improve patient care and treatment practices.
Contextual Explanation Networks
Our results on image and text classification and survival analysis tasks demonstrate that CENs are not only competitive with the state-of-the-art methods but also offer additional insights behind each prediction, that can be valuable for decision support.
Ranked #20 on
Sentiment Analysis
on IMDb
Image Classification
Interpretability Techniques for Deep Learning
+5
Parallel Markov Chain Monte Carlo for the Indian Buffet Process
In this paper we present a novel algorithm to perform asymptotically exact parallel Markov chain Monte Carlo inference for Indian Buffet Process models.
Science Question Answering using Instructional Materials
We provide a solution for elementary science test using instructional materials.
Bayesian Nonparametric Kernel-Learning
In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels.
Large-scale randomized-coordinate descent methods with non-separable linear constraints
We develop randomized (block) coordinate descent (CD) methods for linearly constrained convex optimization.
