Explore the latest peer-reviewed research with AI-generated summaries, key findings, and insights for easy understanding. All content is legally sourced from academic metadata with links to original papers.
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. Then we provide a advanced and elaborated tax...
Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks (DNNs) training, and they usually train a DNN for each single visual recognition task, leading to a laboriou...
The author has spent a large part of his research career studying spoken language processing in bilinguals. Here, the author showed evidence for a number of effects that occur when guest words are rec...
This paper employs the Auto-Encoding Variational Bayes (AEVB) estimator based on Stochastic Gradient Variational Bayes (SGVB), designed to optimize recognition models for challenging posterior distrib...
Abstract We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show the practical relevance of our results in a simulation...
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability...
List of figures Preface and acknowledgements Conventions List of abbreviations 1. The development of social deictics 7.
Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The identified biomarkers s...
April 8, 2009Groups at MIT and NYU have collected a dataset of millions of tiny colour images from the web. The CIFAR-10 set has 6000 examples of each of 10 classes and the CIFAR-100 set has 600 examp...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. Applied to a advanced im...
Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The second observation is emergency vehicle have distinct siren...
In an industrial maintenance context, degradation diagnosis is the problem of determining the current level of degradation of operating machines based on measurements. The proposed method requires tha...
Identifying a maximum independent set is a fundamental NP-hard problem. Therefore, we use a population-based genetic algorithm to evolve the model’s parameters instead.
Abstract—We describe the design of Kaldi, a free, open-source toolkit for speech recognition research. Kaldi is written is C++, and the core library supports modeling of arbitrary phonetic-context siz...
Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. We then demonstrate the difficulty of recognizing these digits when...
CUB-200-2011 is an extended version of CUB-200 , a challenging dataset of 200 bird species. Images and annotations were filtered by mul- tiple users of Mechanical Turk.
This paper presents SimCLR: a simple framework for contrastive learning of visual representations. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1...
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Goog...
Abstract While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. In particular, we observe that performance is...
Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. BILBY has additional functionality to do population studies using hierarchical Bayesian modeling.