140 citations · 318 across the 9 of their papers we have counts for
10 papers
Self-StrAE at SemEval-2024 Task 1: Making Self-Structuring AutoEncoders Learn More With Less
Mattia Opper, N. Siddharth
This paper presents two simple improvements to the Self-Structuring AutoEncoder (Self-StrAE). Firstly, we show that including reconstruction to the vocabulary as an auxiliary objec…
Gradient Matching for Domain Generalization
Yuge Shi, Jeffrey Seely, Philip H. S. Torr +4
Machine learning systems typically assume that the distributions of training and test sets match closely. However, a critical requirement of such systems in the real world is their…
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Yuge Shi, N. Siddharth, Brooks Paige +1
Learning generative models that span multiple data modalities, such as vision and language, is often motivated by the desire to learn more useful, generalisable representations tha…
Disentangling Disentanglement in Variational Autoencoders
Emile Mathieu, Tom Rainforth, N. Siddharth +1
We develop a generalisation of disentanglement in VAEs---decomposition of the latent representation---characterising it as the fulfilment of two factors: a) the latent encodings of…
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent +5
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the dat…
Video In Sentences Out
Andrei Barbu, Alexander Bridge, Zachary Burchill +15
We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as…
Large-Scale Automatic Labeling of Video Events with Verbs Based on Event-Participant Interaction
Andrei Barbu, Alexander Bridge, Dan Coroian +12
We present an approach to labeling short video clips with English verbs as event descriptions. A key distinguishing aspect of this work is that it labels videos with verbs that des…
Seeing Unseeability to See the Unseeable
Siddharth Narayanaswamy, Andrei Barbu, Jeffrey Mark Siskind
We present a framework that allows an observer to determine occluded portions of a structure by finding the maximum-likelihood estimate of those occluded portions consistent with v…
Video In Sentences Out
Andrei Barbu, Alexander Bridge, Zachary Burchill +15
We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as…
Simultaneous Object Detection, Tracking, and Event Recognition
Andrei Barbu, Aaron Michaux, Siddharth Narayanaswamy +1
The common internal structure and algorithmic organization of object detection, detection-based tracking, and event recognition facilitates a general approach to integrating these…