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Found 8 papers, 3 papers with code
An Empirical study to understand the Compositional Prowess of Neural Dialog Models
In this work, we examine the problems associated with neural dialog models under the common theme of compositionality.
SeeGULL Multilingual: a Dataset of Geo-Culturally Situated Stereotypes
While generative multilingual models are rapidly being deployed, their safety and fairness evaluations are largely limited to resources collected in English.
Harm Amplification in Text-to-Image Models
Text-to-image (T2I) models have emerged as a significant advancement in generative AI; however, there exist safety concerns regarding their potential to produce harmful image outputs even when users input seemingly safe prompts.
Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport
Predicting how distributions over discrete variables vary over time is a common task in time series forecasting.
WriterForcing: Generating more interesting story endings
In this paper, we propose models which generate more diverse and interesting outputs by 1) training models to focus attention on important keyphrases of the story, and 2) promoting generation of non-generic words.
Low-rank geometric mean metric learning
We propose a low-rank approach to learning a Mahalanobis metric from data.
A two-dimensional decomposition approach for matrix completion through gossip
The problem of matrix completion especially uses it to decompose a sparse matrix into two non sparse, low rank matrices which can then be used to predict unknown entries of the original matrix.
MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings
E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell billions of products.
