87 citations · 191 across the 11 of their papers we have counts for
11 papers
Multi-entity Video Transformers for Fine-Grained Video Representation Learning
Matthew Walmer, Rose Kanjirathinkal, Kai Sheng Tai +3
The area of temporally fine-grained video representation learning focuses on generating frame-by-frame representations for temporally dense tasks, such as fine-grained action phase…
Analyzing Populations of Neural Networks via Dynamical Model Embedding
Jordan Cotler, Kai Sheng Tai, Felipe Hernández +2
A core challenge in the interpretation of deep neural networks is identifying commonalities between the underlying algorithms implemented by distinct networks trained for the same…
Spartan: Differentiable Sparsity via Regularized Transportation
Kai Sheng Tai, Taipeng Tian, Ser-Nam Lim
We present Spartan, a method for training sparse neural network models with a predetermined level of sparsity. Spartan is based on a combination of two techniques: (1) soft top-k m…
An End-to-End Earthquake Detection Method for Joint Phase Picking and Association using Deep Learning
Weiqiang Zhu, Kai Sheng Tai, S. Mostafa Mousavi +2
Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of the…
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
Kai Sheng Tai, Peter Bailis, Gregory Valiant
Self-training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we re…
Equivariant Transformer Networks
Kai Sheng Tai, Peter Bailis, Gregory Valiant
How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network? We propose Equivariant Transformers (ETs), a family…
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries
Edward Gan, Jialin Ding, Kai Sheng Tai +2
Interactive analytics increasingly involves querying for quantiles over sub-populations of high cardinality datasets. Data processing engines such as Druid and Spark use mergeable…
Sketching Linear Classifiers over Data Streams
Kai Sheng Tai, Vatsal Sharan, Peter Bailis +1
We introduce a new sub-linear space sketch---the Weight-Median Sketch---for learning compressed linear classifiers over data streams while supporting the efficient recovery of larg…
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
Vatsal Sharan, Kai Sheng Tai, Peter Bailis +1
What learning algorithms can be run directly on compressively-sensed data? In this work, we consider the question of accurately and efficiently computing low-rank matrix or tensor…
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Kai Sheng Tai, Richard Socher, Christopher D. Manning
Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computa…
Detecting gravitational waves from highly eccentric compact binaries
Kai Sheng Tai, Sean T. McWilliams, Frans Pretorius
In dense stellar regions, highly eccentric binaries of black holes and neutron stars can form through various n-body interactions. Such a binary could emit a significant fraction o…