Published in IEEE TPAMI, 2025
Theoretical considerations and evaluation of split and distillation points.
Recommended citation: Alon Harell, Yalda Foroutan, Nilesh A. Ahuja, Parual Datta, Bhavya Kanzariya, V. Srinivasa Somayazulu, Omesh Tickoo, Anderson de Andrade, & Ivan V. Bajic. (2025). "Rate-distortion theory in coding for machines and its applications." IEEE TPAMI.
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Published in arXiv, 2020
Graph representations using a learnable attention mechanism to sample the neighbourhood of a graph.
Recommended citation: Anderson de Andrade, & Chen Liu. (2020). "Graph representation learning network via adaptive sampling." arXiv:2006.04637.
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Published in arXiv, 2014
Evaluate the performance impact of optimization algorithms, activation functions, dropout, and maxout networks, in CNNs.
Recommended citation: Anderson de Andrade. (2014). "Best practices for convolutional neural networks applied to object recognition in images." arXiv:1910.13029.
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Published in arXiv, 2013
Benchmark of stochastic gradient descent and Nesterov’s accelerated gradient for text classification.
Recommended citation: Anderson de Andrade. (2013). "A comparison of neural network training methods for text classification." arXiv:1910.12674.
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Published in ICME Workshop on Coding for Machines, 2024
Task reconstruction loss acts as a regularizer, increasing rate-distortion performance in coding for humans and machines.
Recommended citation: Anderson de Andrade, & Ivan Bajić. (2024). "Towards task-compatible compressible representations. ICME Workshop on Coding for Machines." ICME Workshop on Coding for Machines.
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Published in ICME Workshop on Coding for Machines, 2023
A comparison between conditional and residual entropy codecs for a two-channel systems of tasks with nested information.
Recommended citation: Anderson de Andrade, Alon Harell, & Ivan Bajić. (2023). "Conditional and residual methods in scalable coding for humans and machines." ICME Workshop on Coding for Machines.
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Published in NAACL Conference on Human Language Technologies: Industry Papers, 2021
Unified batch and online transformer inference.
Recommended citation: Amir Ganiev, Colt Chapin, Anderson de Andrade, & Chen Liu. (2021). "An architecture for accelerated large-scale inference of transformer-based language models." NAACL Conference on Human Language Technologies: Industry Papers.
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Published in EMNLP Workshop on Noisy User-Generated Text, 2019
Sentence embeddings augmented by universal parts-of-speech tags evaluated on low-resource languages.
Recommended citation: Chen Liu, Anderson de Andrade, & Muhammad Osama. (2019). "Exploring multilingual syntactic sentence representations." EMNLP Workshop on Noisy User-Generated Text.
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