CROWD COUNTING, Machine Learning Leave a comment

Completely Self-Supervised Crowd Counting via Distribution Matching

Dense crowd counting is a challenging task that demands millions of head annotations for training models. Though existing self-supervised approaches could learn good representations, they require some labeled data to map these features to the end task of density estimation… We mitigate this issue with the proposed paradigm of complete self-supervision, which does not need even a single labeled image. The only input required to train, apart from a large set of unlabeled crowd images, is the approximate upper limit […]

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Machine Learning Leave a comment

Multi-Referenced Training for Dialogue Response Generation

In open-domain dialogue response generation, a dialogue context can be continued with diverse responses, and the dialogue models should capture such one-to-many relations. In this work, we first analyze the training objective of dialogue models from the view of Kullback-Leibler divergence (KLD) and show that the gap between the real world probability distribution and the single-referenced data’s probability distribution prevents the model from learning the one-to-many relations efficiently… Then we explore approaches to multi-referenced training in two aspects. Data-wise, we […]

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NLP, Semantic Similarity

Issue #99 – Training Neural Machine Translation with Semantic Similarity

17 Sep20 Issue #99 – Training Neural Machine Translation with Semantic Similarity Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction The standard way of training Neural Machine Translation (NMT) systems is by Maximum Likelihood Estimation (MLE), and although there have been experiments in the past to optimize systems directly in order to improve particular evaluation metrics, these were of limited success. Of course, using BLEU is not ideal due to the fact that it fails to account for […]

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Machine Learning Leave a comment

Self-Supervised Annotation of Seismic Images using Latent Space Factorization

Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper, we develop a framework to automate annotating pixels of a seismic image to delineate geological structural elements given image-level labels assigned to each image… Our framework factorizes the latent space of a deep encoder-decoder network by projecting the latent space to learned sub-spaces. Using constraints in the pixel space, the seismic image is further […]

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CONVERSATIONAL RESPONSE SELECTION, Machine Learning Leave a comment

Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection

In this paper, we study the task of selecting optimal response given user and system utterance history in retrieval-based multi-turn dialog systems. Recently, pre-trained language models (e.g., BERT, RoBERTa, and ELECTRA) have shown significant improvements in various natural language processing tasks… This and similar response selection tasks can also be solved using such language models by formulating them as dialog-response binary classification tasks. Although existing works using this approach successfully obtained state-of-the-art results, we observe that language models trained in […]

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ADVERSARIAL TRAINING, Machine Learning Leave a comment

Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent

Adversarial training, especially projected gradient descent (PGD), has been the most successful approach for improving robustness against adversarial attacks. After adversarial training, gradients of models with respect to their inputs are meaningful and interpretable by humans… However, the concept of interpretability is not mathematically well established, making it difficult to evaluate it quantitatively. We define interpretability as the alignment of the model gradient with the vector pointing toward the closest point of the support of the other class. We propose […]

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Machine Learning, TEXT CLASSIFICATION Leave a comment

Modern Methods for Text Generation

Synthetic text generation is challenging and has limited success. Recently, a new architecture, called Transformers, allow machine learning models to understand better sequential data, such as translation or summarization… BERT and GPT-2, using Transformers in their cores, have shown a great performance in tasks such as text classification, translation and NLI tasks. In this article, we analyse both algorithms and compare their output quality in text generation tasks. (read more) PDF Abstract Visit source site

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Machine Learning Leave a comment

Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

Using logical clauses to represent patterns, Tsetlin machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. A team of Tsetlin automata (TAs) composes each clause, thus driving the entire learning process… These are rewarded/penalized according to three local rules that optimize global behaviour. Each clause votes for or against a particular class, with classification resolved using a majority vote. In the parallel and asynchronous architecture that we propose here, […]

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CONTINUAL LEARNING, Machine Learning Leave a comment

Meta-Learning with Sparse Experience Replay for Lifelong Language Learning

Lifelong learning requires models that can continuously learn from sequential streams of data without suffering catastrophic forgetting due to shifts in data distributions. Deep learning models have thrived in the non-sequential learning paradigm; however, when used to learn a sequence of tasks, they fail to retain past knowledge and learn incrementally… We propose a novel approach to lifelong learning of language tasks based on meta-learning with sparse experience replay that directly optimizes to prevent forgetting. We show that under the […]

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DOMAIN GENERALIZATION, Machine Learning Leave a comment

Heterogeneous Domain Generalization via Domain Mixup

One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. In this work, we focus on the problem of heterogeneous domain generalization which aims to improve the generalization capability across different tasks, which is, how to learn a DCNN model with multiple domain data such that the trained feature extractor can be generalized to supporting recognition of novel categories in a novel target domain… To solve this problem, we propose a novel heterogeneous […]

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