DiscoFuse A Large Scale Dataset for Discourse Based Sentence Fusion

Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically-generating fusion examples from raw text and present DiscoFuse, a large scale dataset for discourse-based sentence fusion.… Read the rest

Disentangled Person Image Generation

Aims to generate novel, yet realistic, images of persons based on a novel, two-stage reconstruction pipeline. Multi-branched reconstruction network is proposed to disentangle and encode the three factors into embedding features, which are then combined to re-compose the input image itself.… Read the rest

Diversified Arbitrary Style Transfer via Deep Feature Perturbation

Image style transfer is an underdetermined problem, where a large number of solutions can satisfy the same constraint (the content and style) The key idea of our method is an operation called deep feature perturbation (DFP), which uses an orthogonal random noise matrix to perturb the deep image feature maps while keeping the original style information unchanged.… Read the rest

Document Image Classification with Intra Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks

A region-based Deep Convolutional Neural Network framework is proposed for document structure learning. The proposed method achieves state-of-the-art accuracy of 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarks set by existing algorithms. The contribution of this work involves efficient training of region based classifiers and effective ensembling for document image classification… A primary level of `inter-domain’ transfer learning is used by exporting weights from a pre-trained VGG16 architecture on the ImageNet dataset to train a document classifier on whole document images.… Read the rest

Don t Just Scratch the Surface Enhancing Word Representations for Korean with Hanja

We propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i.e. Hanja) We employ cross-lingual transfer learning in training word representations by leveraging the fact that Hanja is closely related to Chinese. We evaluate the intrinsic quality of representations learned through our approach using the word analogy and similarity tests.… Read the rest

Double Double Descent On Generalization Errors in Transfer Learning between Linear Regression Tasks

We study the transfer learning process between two linear regression problems. An important and timely special case is when the regressors are overparameterized and perfectly interpolate their training data. We examine a parameter transfer mechanism whereby a subset of the parameters of the target task solution are constrained to the values learned for a related source task.… Read the rest

DRCD a Chinese Machine Reading Comprehension Dataset

In this paper, we introduce DRCD (Delta Reading Comprehension Dataset), an open domain traditional Chinese machine reading comprehension (MRC) dataset. The dataset contains 10,014 paragraphs from 2,108 Wikipedia articles and 30,000+ questions generated by annotators. We build a baseline model that achieves an F1 score of 89.59%.

Effective Cross lingual Transfer of Neural Machine Translation Models without Shared Vocabularies

Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT) But applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pre-trained NMT model to a new, unrelated language. We relieve the vocabulary mismatch by using cross-lingual word embedding, train a more language-agnostic encoder by injecting artificial noises, and generate synthetic data easily from the pre-training data without back-translation.… Read the rest

EfficientNet Rethinking Model Scaling for Convolutional Neural Networks

Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets.… Read the rest

HOUDINI Lifelong Learning as Program Synthesis

We present a neurosymbolic framework for the lifelong learning of algorithmic tasks that mix perception and procedural reasoning. Reusing high-level concepts across domains and learning complex procedures are key challenges in lifelong learning. We show that a program synthesis approach that combines gradient descent with combinatorial search over programs can be a more effective response to these challenges than purely neural methods.… Read the rest

Motion Planning Networks

Motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. Motion Planning Networks (MPNet) is a neural network-based novel planning algorithm.… Read the rest

Exploring the Limits of Transfer Learning with a Unified Text to Text Transformer

Transfer learning is where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task. We introduce a unified framework that converts every language problem into a text-to-text format. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.… Read the rest

DistilBERT a distilled version of BERT smaller faster cheaper and lighter

As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts.… Read the rest

Talking Heads Attention

“Talking-heads attention” is a variation on multi-head attention. It includes linearprojections across the attention-heads dimension, immediately before and after the softmax operation. It leads to better perplexities on masked language modeling tasks, aswell as better quality when transfer-learning to language comprehension and question answering tasks.

A Unified Neural Architecture for Instrumental Audio Tasks

Within Music Information Retrieval (MIR), prominent tasks typically call for specialised methods. Conditional Generative Adversarial Networks (cGANs) have been shown to be highly versatile in learning general image-to-image translations. In doing so, we demonstrate the potential of such flexible techniques to unify MIR tasks, promote efficient transfer learning, and converge research to the improvement of powerful, general methods.… Read the rest

Easy Transfer Learning By Exploiting Intra domain Structures

Transfer learning aims at transferring knowledge from a well-labeled domain to a similar but different domain with limited or no labels. Existing learning-based methods often involve intensive model selection and hyperparameter tuning to obtain good results. This would restrict wide applicability of transfer learning especially in computationally-constraint devices such as wearables.… Read the rest

Conversational Transfer Learning for Emotion Recognition

Recognizing emotions in conversations is a challenging task due to the presence of contextual dependencies governed by self- and inter-personal influences. Given the large amount of available conversational data, we investigate whether generative conversational models can be leveraged to transfer affective knowledge for detecting emotions in context.… Read the rest

Generative Pre Training for Speech with Autoregressive Predictive Coding

Autoregressive predictive coding (APC) is a recently proposed self-supervised objective. We pre-train APC on large-scale unlabeled data and conduct transfer learning experiments on three speech applications that require different information about speech characteristics. APC not only outperforms surface features and other popular representation learning methods on all three tasks, but is also effective at reducing downstream labeled data size and model parameters.… Read the rest

Construction industry

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Build Construction

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Life Lack Meaning

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Construction industry

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Content Marketing

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Construction industry

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