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Found 16 papers, 5 papers with code
Substance or Style: What Does Your Image Embedding Know?
We find that image-text models (CLIP and ALIGN) are better at recognizing new examples of style transfer than masking-based models (CAN and MAE).
Approximating a RUM from Distributions on k-Slates
In this work we consider the problem of fitting Random Utility Models (RUMs) to user choices.
CARLS: Cross-platform Asynchronous Representation Learning System
In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.
Graph Autoencoders with Deconvolutional Networks
Recent studies have indicated that Graph Convolutional Networks (GCNs) act as a \emph{low pass} filter in spectral domain and encode smoothed node representations.
Adversarial Robustness Across Representation Spaces
In this work we extend the above setting to consider the problem of training of deep neural networks that can be made simultaneously robust to perturbations applied in multiple natural representation spaces.
Surprise: Result List Truncation via Extreme Value Theory
Work in information retrieval has largely been centered around ranking and relevance: given a query, return some number of results ordered by relevance to the user.
Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study
Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning.
BusTr: Predicting Bus Travel Times from Real-Time Traffic
We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided.
Choppy: Cut Transformer For Ranked List Truncation
Work in information retrieval has traditionally focused on ranking and relevance: given a query, return some number of results ordered by relevance to the user.
Reverse Engineering Configurations of Neural Text Generation Models
This paper seeks to develop a deeper understanding of the fundamental properties of neural text generations models.
Graph Agreement Models for Semi-Supervised Learning
To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features.
Preventing Adversarial Use of Datasets through Fair Core-Set Construction
We propose improving the privacy properties of a dataset by publishing only a strategically chosen "core-set" of the data containing a subset of the instances.
Graph-RISE: Graph-Regularized Image Semantic Embedding
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.
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Image Classification
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Learning a Mixture of Two Multinomial Logits
In this model, a user is offered a slate of choices (a subset of a finite universe of $n$ items), and selects exactly one item from the slate, each with probability proportional to its (positive) weight.
Smart Reply: Automated Response Suggestion for Email
In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply.
