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Found 12 papers, 8 papers with code
Multimodal Phased Transformer for Sentiment Analysis
We evaluate our model with three sentiment analysis datasets and achieve comparable or superior performance compared with the existing methods, with a 90% reduction in the number of parameters.
Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods
In this work, we systematically study the problem of personalized text-to-image generation, where the output image is expected to portray information about specific human subjects.
Batch Model Consolidation: A Multi-Task Model Consolidation Framework
During the $\textit{consolidation}$ phase, we combine the learned knowledge on 'batches' of $\textit{expert models}$ using a $\textit{batched consolidation loss}$ in $\textit{memory}$ data that aggregates all buffers.
Lightweight Learner for Shared Knowledge Lifelong Learning
We propose a new Shared Knowledge Lifelong Learning (SKILL) challenge, which deploys a decentralized population of LL agents that each sequentially learn different tasks, with all agents operating independently and in parallel.
Reproducibility Requires Consolidated Artifacts
Machine learning is facing a 'reproducibility crisis' where a significant number of works report failures when attempting to reproduce previously published results.
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network
Inspired by the recent success of prototypical and contrastive learning frameworks for both improving robustness and learning nuance invariant representations, we propose a training framework, $\textbf{Supervised Contrastive Prototype Learning}$ (SCPL).
Implicit Feature Decoupling with Depthwise Quantization
We use DQ in the context of Hierarchical Auto-Encoder and train end-to-end on an image feature representation.
Graph Conditioned Sparse-Attention for Improved Source Code Understanding
The fusion between a graph representation like Abstract Syntax Tree (AST) and a source code sequence makes the use of current approaches computationally intractable for large input sequence lengths.
GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization
SCG is the result of the early fusion between a source code snippet and the AST representation.
Learning Hyperbolic Representations of Topological Features
Learning task-specific representations of persistence diagrams is an important problem in topological data analysis and machine learning.
GN-Transformer: Fusing AST and Source Code information in Graph Networks
As opposed to natural languages, source code understanding is influenced by grammar relations between tokens regardless of their identifier name.
Depthwise Discrete Representation Learning
Recent advancements in learning Discrete Representations as opposed to continuous ones have led to state of art results in tasks that involve Language, Audio and Vision.
