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Found 24 papers, 6 papers with code
Tackling Fake News Detection by Interactively Learning Representations using Graph Neural Networks
Easy access, variety of content, and fast widespread interactions are some of the reasons that have made social media increasingly popular in today’s society.
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks
We view fake news detection as reasoning over the relations between sources, articles they publish, and engaging users on social media in a graph framework.
Aligning Large Language Models with Recommendation Knowledge
Operations such as Masked Item Modeling (MIM) and Bayesian Personalized Ranking (BPR) have found success in conventional recommender systems.
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning
Zero-shot learning (ZSL) is a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges remain.
Interactively Learning Social Media Representations Improves News Source Factuality Detection
The rise of social media has enabled the widespread propagation of fake news, text that is published with an intent to spread misinformation and sway beliefs.
An Interactive Framework for Profiling News Media Sources
The recent rise of social media has led to the spread of large amounts of fake and biased news, content published with the intent to sway beliefs.
Density Weighting for Multi-Interest Personalized Recommendation
When the data distribution is highly skewed, the gains observed by learning multiple representations diminish since the model dominates on head items/interests, leading to poor performance on tail items.
Better Generalization with Semantic IDs: A case study in Ranking for Recommendations
We showcase how we use them as a replacement of item IDs in a resource-constrained ranking model used in an industrial-scale video sharing platform.
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings.
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback
We explore multiple types of help players can give to the AI to guide it and analyze the impact of this help in AI behavior, resulting in performance improvements.
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks.
Pseudo-OOD training for robust language models
The model is fine-tuned by introducing a new regularization loss that separates the embeddings of IND and OOD data, which leads to significant gains on the OOD prediction task during testing.
A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology
Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that is representative of UAV images.
Efficient Feature Transformations for Discriminative and Generative Continual Learning
However, the growth in the number of additional parameters of many of these types of methods can be computationally expensive at larger scales, at times prohibitively so.
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning
Zero-shot learning (ZSL) has been shown to be a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges still remain.
Counterfactual Representation Learning with Balancing Weights
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type.
WAFFLe: Weight Anonymized Factorization for Federated Learning
Experiments on MNIST, FashionMNIST, and CIFAR-10 demonstrate WAFFLe's significant improvement to local test performance and fairness while simultaneously providing an extra layer of security.
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors
Naively trained neural networks tend to experience catastrophic forgetting in sequential task settings, where data from previous tasks are unavailable.
Graph Representation Learning via Ladder Gamma Variational Autoencoders
In addition to leveraging the representational power of multiple layers of stochastic variables via the ladder VAE architecture, our framework offers the following benefits: (1) Unlike existing ladder VAE architectures based on real-valued latent variables, the gamma-distributed latent variables naturally result in non-negativity and sparsity of the learned embeddings, and facilitate their direct interpretation as membership of nodes into (possibly multiple) communities/topics; (2) A novel recognition model for our gamma ladder VAE architecture allows fast inference of node embeddings; and (3) The framework also extends naturally to incorporate node side information (features and/or labels).
Survival Cluster Analysis
As a result, there is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles, while jointly accounting for accurate individualized time-to-event predictions.
Stochastic Blockmodels meet Graph Neural Networks
Although we develop this framework for a particular type of SBM, namely the \emph{overlapping} stochastic blockmodel, the proposed framework can be adapted readily for other types of SBMs.
Improving Natural Language Interaction with Robots Using Advice
Over the last few years, there has been growing interest in learning models for physically grounded language understanding tasks, such as the popular blocks world domain.
Deep Topic Models for Multi-label Learning
We present a probabilistic framework for multi-label learning based on a deep generative model for the binary label vector associated with each observation.
