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Found 11 papers, 4 papers with code
Towards Task-Compatible Compressible Representations
We evaluate the impact of this idea in the context of input reconstruction more rigorously and extended it to other computer vision tasks.
Base Layer Efficiency in Scalable Human-Machine Coding
A basic premise in scalable human-machine coding is that the base layer is intended for automated machine analysis and is therefore more compressible than the same content would be for human viewing.
Rate-Distortion Theory in Coding for Machines and its Application
To meet this growing demand, several methods have been developed for image and video coding for machines.
Conditional and Residual Methods in Scalable Coding for Humans and Machines
We present methods for conditional and residual coding in the context of scalable coding for humans and machines.
Rate-Distortion in Image Coding for Machines
In our experiments we show the trade-off between the human and machine sides of such a scalable model, and discuss the benefit of using deeper layers for training in that regard.
An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models
We used a BERT model that was fine-tuned for emotion analysis, returning a probability distribution of emotions given a paragraph.
Graph Representation Learning Network via Adaptive Sampling
Graph Attention Network (GAT) and GraphSAGE are neural network architectures that operate on graph-structured data and have been widely studied for link prediction and node classification.
Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images
We design 4 models of convolutional neural networks that explore characteristics such as depth, number of feature maps, size and overlap of kernels, pooling regions, and different subsampling techniques.
A Comparison of Neural Network Training Methods for Text Classification
We study the impact of neural networks in text classification.
DENS: A Dataset for Multi-class Emotion Analysis
Our results show that the dataset provides a novel opportunity in emotion analysis that requires moving beyond existing sentence-level techniques.
Exploring Multilingual Syntactic Sentence Representations
We study methods for learning sentence embeddings with syntactic structure.
