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Found 8 papers, 1 papers with code
Abusive Language Detection using Syntactic Dependency Graphs
Automated detection of abusive language online has become imperative.
VisualLens: Personalization through Visual History
We hypothesize that a user's visual history with images reflecting their daily life, offers valuable insights into their interests and preferences, and can be leveraged for personalization.
CoDi: Conversational Distillation for Grounded Question Answering
This is a typical on-device scenario for specialist SLMs, allowing for open-domain model responses, without requiring the model to "memorize" world knowledge in its limited weights.
On the Equivalence of Graph Convolution and Mixup
We establish this equivalence mathematically by demonstrating that graph convolution networks (GCN) and simplified graph convolution (SGC) can be expressed as a form of Mixup.
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning
Meta-training allows one to leverage smaller models for few-shot generalization in a domain-general and task-agnostic manner; however, these methods alone results in models that may not have sufficient parameterization or knowledge to adapt quickly to a large variety of tasks.
Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features
Fine-tuning with Q-Score regularization can boost the linear probing accuracy of SSL models by up to 5. 8% on ImageNet-100 and 3. 7% on ImageNet-1K compared to their baselines.
An Induced Multi-Relational Framework for Answer Selection in Community Question Answer Platforms
We develop a novel induced relational graph convolutional network (IR-GCN) framework to address the question.
