10 citations · 17 across the 12 of their papers we have counts for
13 papers
Understanding Rate-Distortion Performance in Distributed Transformer Inference
Anderson de Andrade, Alon Harell, Ivan V. Bajić
Transformers achieve superior performance on many tasks, but impose heavy compute and memory requirements during inference. This inference can be made more efficient by partitionin…
Lossy Common Information in a Learnable Gray-Wyner Network
Anderson de Andrade, Alon Harell, Ivan V. Bajić
Many computer vision tasks share substantial overlapping information, yet conventional codecs tend to ignore this, leading to redundant and inefficient representations. The Gray-Wy…
Mutual Information Bounds for Lossy Common Information
Anderson de Andrade
We show the mutual information between the targets in a Gray-Wyner Network as a bound that separates Wyner's lossy common information and Gács-Körner lossy common information. The…
Towards Task-Compatible Compressible Representations
Anderson de Andrade, Ivan Bajić
We identify an issue in multi-task learnable compression, in which a representation learned for one task does not positively contribute to the rate-distortion performance of a diff…
Base Layer Efficiency in Scalable Human-Machine Coding
Yalda Foroutan, Alon Harell, Anderson de Andrade +1
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…
Rate-Distortion Theory in Coding for Machines and its Application
Alon Harell, Yalda Foroutan, Nilesh Ahuja +6
Recent years have seen a tremendous growth in both the capability and popularity of automatic machine analysis of images and video. As a result, a growing need for efficient compre…
Conditional and Residual Methods in Scalable Coding for Humans and Machines
Anderson de Andrade, Alon Harell, Yalda Foroutan +1
We present methods for conditional and residual coding in the context of scalable coding for humans and machines. Our focus is on optimizing the rate-distortion performance of the…
Rate-Distortion in Image Coding for Machines
Alon Harell, Anderson De Andrade, Ivan V. Bajic
In recent years, there has been a sharp increase in transmission of images to remote servers specifically for the purpose of computer vision. In many applications, such as surveill…
Graph Representation Learning Network via Adaptive Sampling
Anderson de Andrade, Chen Liu
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 classif…
Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images
Anderson de Andrade
This research project studies the impact of convolutional neural networks (CNN) in image classification tasks. We explore different architectures and training configurations with t…
A Comparison of Neural Network Training Methods for Text Classification
Anderson de Andrade
We study the impact of neural networks in text classification. Our focus is on training deep neural networks with proper weight initialization and greedy layer-wise pretraining. Re…
DENS: A Dataset for Multi-class Emotion Analysis
Chen Liu, Muhammad Osama, Anderson de Andrade
We introduce a new dataset for multi-class emotion analysis from long-form narratives in English. The Dataset for Emotions of Narrative Sequences (DENS) was collected from both cla…
Exploring Multilingual Syntactic Sentence Representations
Chen Liu, Anderson de Andrade, Muhammad Osama
We study methods for learning sentence embeddings with syntactic structure. We focus on methods of learning syntactic sentence-embeddings by using a multilingual parallel-corpus au…