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Publications
MICo: Improved representations via sampling-based state similarity for Markov decision processes
We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an effe… (voir plus)ctive means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analyses, we provide empirical evidence that learning this distance alongside the value function yields structured and informative representations, including strong results on the Arcade Learning Environment benchmark.
We present a new behavioural distance over the state space of a Markov decision process, and demonstrate the use of this distance as an eff… (voir plus)ective means of shaping the learnt representations of deep reinforcement learning agents. While existing notions of state similarity are typically difficult to learn at scale due to high computational cost and lack of sample-based algorithms, our newly-proposed distance addresses both of these issues. In addition to providing detailed theoretical analysis
Motivated by estimation problems arising in autonomous vehicles and decentralized control of unmanned aerial vehicles, we consider multi-age… (voir plus)nt estimation and filtering problems in which multiple agents generate state estimates based on decentralized information and the objective is to minimize a coupled mean-squared error which we call team mean-square error. We call the resulting estimates as minimum team mean-squared error (MTMSE) estimates. We show that MTMSE estimates are different from minimum mean-squared error (MMSE) estimates. We derive closed-form expressions for MTMSE estimates, which are linear function of the observations where the corresponding gain depends on the weight matrix that couples the estimation error. We then consider a filtering problem where a linear stochastic process is monitored by multiple agents which can share their observations (with delay) over a communication graph. We derive expressions to recursively compute the MTMSE estimates. To illustrate the effectiveness of the proposed scheme we consider an example of estimating the distances between vehicles in a platoon and show that MTMSE estimates significantly outperform MMSE estimates and consensus Kalman filtering estimates.
Learning models that generalize under different distribution shifts in medical imaging has been a long-standing research challenge. There ha… (voir plus)ve been several proposals for efficient and robust visual representation learning among vision research practitioners, especially in the sensitive and critical biomedical domain. In this paper, we propose an idea for out-of-distribution generalization of chest X-ray pathologies that uses a simple balanced batch sampling technique. We observed that balanced sampling between the multiple training datasets improves the performance over baseline models trained without balancing. Code for this work is available on Github. 1
Multilevel Approaches for the Critical Node Problem
Tandem spoken language understanding 001 (SLU) systems suffer from the so-called 002 automatic speech recognition (ASR) error 003 propagatio… (voir plus)n problem. Additionally, as the 004 ASR is not optimized to extract semantics, but 005 solely the linguistic content, relevant semantic 006 cues might be left out of its transcripts. In 007 this work, we propose a multimodal language 008 understanding (MLU) architecture to mitigate 009 these problems. Our solution is based on 010 two compact unidirectional long short-term 011 memory (LSTM) models that encode speech 012 and text information. A fusion layer is also 013 used to fuse audio and text embeddings. 014 Two fusion strategies are explored: a simple 015 concatenation of these embeddings and a 016 cross-modal attention mechanism that learns 017 the contribution of each modality. The first 018 approach showed to be the optimal solution 019 to robustly extract semantic information from 020 audio-textual data. We found that attention 021 is less effective at testing time when the text 022 modality is corrupted. Our model is evaluated 023 on three SLU datasets and robustness is tested 024 using ASR outputs from three off-the-shelf 025 ASR engines. Results show that the proposed 026 approach effectively mitigates the ASR error 027 propagation problem for all datasets. 028
Neural Approximate Sufficient Statistics for Implicit Models
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation… (voir plus) of likelihood function is intractable but sampling / simulating data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual information maximizing representation of the data. This representation is computed by a deep neural network trained by a joint statistic-posterior learning strategy. We apply our approach to both traditional approximate Bayesian computation (ABC) and recent neural likelihood approaches, boosting their performance on a range of tasks.
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from de generation: generated… (voir plus) text is repetitive, generic, self-inconsistent, and lacking commonsense. The empirical analyses on sentence-level attention patterns reveal that neural text degeneration may be associated with insufficient learning of inductive biases by the attention mechanism. Our findings motivate on-the-fly attention modularization, a simple but effective method for injecting inductive biases into attention computation during inference. The resulting text produced by the language model with attention modularization can yield enhanced diversity and commonsense reasoning while maintaining fluency and coherence.
. Justification of the selection of the architecture and hyperparameters of artificial neural networks (ANN), focused on solving various cla… (voir plus)sses of applied problems, is a scientific and methodological problem. Optimizing the selection of ANN hyperparameters allows you to improve the quality and speed of ANN training. Various methods of optimizing the selection of ANN hyper-parameters are known – the use of evolutionary calculations, genetic algorithms, etc., but they require the use of additional software. To optimize the process of selecting ANN hyperparameters, Google Research has developed the KerasTuner software tool. It is a platform for automated search of a set of optimal combinations of hyperparameters. In Kerastuner, you can use various methods - random search, Bayesian optimization, or Hyperband. In the numerical experiments conducted by the author, 14 hyperparameters were varied, including the number of blocks of convolutional layers and the filters forming them, the type of activation function, the parameters of the "dropout" layers, and others. The studied tools demonstrated high efficiency while simultaneously varying more than a dozen optimized parameters of the convolutional network. The calculation time on the Colaboratory platform for the various combined ANN architectures studied, including recurrent RNN networks, was several hours, even with the use of GPU graphics accelerators. For ANN, focused on the processing and recognition of retrospective information, an increase in the quality of recognition was achieved to 80 ... 95%.
The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide … (voir plus)a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents' topical content or authors. The 2021 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2021 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.
