Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization. We establish a unified open-source framework to support fast development of such sophisticated NLP workflows in a composable manner. The framework introduces a uniform data representation to encode heterogeneous results by a wide range of NLP tasks. It offers a large repository of processors for NLP tasks, visualization, and annotation, which can be easily assembled with full interoperability under the unified representation. The highly extensible framework allows plugging in custom processors from external off-the-shelf NLP and deep learning libraries. The whole framework is delivered through two modularized yet integratable open-source projects, namely Forte1 (for workflow infrastructure and NLP function processors) and Stave2 (for user interaction, visualization, and annotation).

A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches make use of cloud computing facilities and do not scale well to autonomous agents with low computational resources. Even in the cloud, they suffer from computational and memory limitations, and they cannot be used to model adequately large physical worlds for agents which assume networks with billions of neurons. These issues are addressed in the last few years by the emerging topic of sparse training, which trains sparse networks from scratch. This paper discusses sparse training state-of-the-art, its challenges and limitations while introducing a couple of new theoretical research directions which has the potential of alleviating sparse training limitations to push deep learning scalability well beyond its current boundaries. Nevertheless, the theoretical advancements impact in complex multi-agents settings is discussed from a real-world perspective, using the smart grid case study.

We present DeepMVI, a deep learning method for missing value imputation in multidimensional time-series datasets. Missing values are commonplace in decision support platforms that aggregate data over long time stretches from disparate sources, and reliable data analytics calls for careful handling of missing data. One strategy is imputing the missing values, and a wide variety of algorithms exist spanning simple interpolation, matrix factorization methods like SVD, statistical models like Kalman filters, and recent deep learning methods. We show that often these provide worse results on aggregate analytics compared to just excluding the missing data. DeepMVI uses a neural network to combine fine-grained and coarse-grained patterns along a time series, and trends from related series across categorical dimensions. After failing with off-the-shelf neural architectures, we design our own network that includes a temporal transformer with a novel convolutional window feature, and kernel regression with learned embeddings. The parameters and their training are designed carefully to generalize across different placements of missing blocks and data characteristics. Experiments across nine real datasets, four different missing scenarios, comparing seven existing methods show that DeepMVI is significantly more accurate, reducing error by more than 50% in more than half the cases, compared to the best existing method. Although slower than simpler matrix factorization methods, we justify the increased time overheads by showing that DeepMVI is the only option that provided overall more accurate analytics than dropping missing values.

We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model's strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-the-art results in terms of image quality and diversity, while enjoying fast learning and better data-efficiency. Further qualitative and quantitative experiments offer us an insight into the model's inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at https://github.com/dorarad/gansformer.

Recently, significant advancements have been made in face recognition technologies using Deep Neural Networks. As a result, companies such as Microsoft, Amazon, and Naver offer highly accurate commercial face recognition web services for diverse applications to meet the end-user needs. Naturally, however, such technologies are threatened persistently, as virtually any individual can quickly implement impersonation attacks. In particular, these attacks can be a significant threat for authentication and identification services, which heavily rely on their underlying face recognition technologies' accuracy and robustness. Despite its gravity, the issue regarding deepfake abuse using commercial web APIs and their robustness has not yet been thoroughly investigated. This work provides a measurement study on the robustness of black-box commercial face recognition APIs against Deepfake Impersonation (DI) attacks using celebrity recognition APIs as an example case study. We use five deepfake datasets, two of which are created by us and planned to be released. More specifically, we measure attack performance based on two scenarios (targeted and non-targeted) and further analyze the differing system behaviors using fidelity, confidence, and similarity metrics. Accordingly, we demonstrate how vulnerable face recognition technologies from popular companies are to DI attack, achieving maximum success rates of 78.0% and 99.9% for targeted (i.e., precise match) and non-targeted (i.e., match with any celebrity) attacks, respectively. Moreover, we propose practical defense strategies to mitigate DI attacks, reducing the attack success rates to as low as 0% and 0.02% for targeted and non-targeted attacks, respectively.

Recommendation models are commonly used learning models that suggest relevant items to a user for e-commerce and online advertisement-based applications. Current recommendation models include deep-learning-based (DLRM) and time-based sequence (TBSM) models. These models use massive embedding tables to store a numerical representation of item's and user's categorical variables (memory-bound) while also using neural networks to generate outputs (compute-bound). Due to these conflicting compute and memory requirements, the training process for recommendation models is divided across CPU and GPU for embedding and neural network executions, respectively. Such a training process naively assigns the same level of importance to each embedding entry. This paper observes that some training inputs and their accesses into the embedding tables are heavily skewed with certain entries being accessed up to 10000x more. This paper tries to leverage skewed embedded table accesses to efficiently use the GPU resources during training. To this end, this paper proposes a Frequently Accessed Embeddings (FAE) framework that exposes a dynamic knob to the software based on the GPU memory capacity and the input popularity index. This framework efficiently estimates and varies the size of the hot portions of the embedding tables within GPUs and reallocates the rest of the embeddings on the CPU. Overall, our framework speeds-up the training of the recommendation models on Kaggle, Terabyte, and Alibaba datasets by 2.34x as compared to a baseline that uses Intel-Xeon CPUs and Nvidia Tesla-V100 GPUs, while maintaining accuracy.

The ability to perform arithmetic tasks is a remarkable trait of human intelligence and might form a critical component of more complex reasoning tasks. In this work, we investigate if the surface form of a number has any influence on how sequence-to-sequence language models learn simple arithmetic tasks such as addition and subtraction across a wide range of values. We find that how a number is represented in its surface form has a strong influence on the model's accuracy. In particular, the model fails to learn addition of five-digit numbers when using subwords (e.g., "32"), and it struggles to learn with character-level representations (e.g., "3 2"). By introducing position tokens (e.g., "3 10e1 2"), the model learns to accurately add and subtract numbers up to 60 digits. We conclude that modern pretrained language models can easily learn arithmetic from very few examples, as long as we use the proper surface representation. This result bolsters evidence that subword tokenizers and positional encodings are components in current transformer designs that might need improvement. Moreover, we show that regardless of the number of parameters and training examples, models cannot seem to learn addition rules that are independent of the length of the numbers seen during training. Abstraction and composition are two important themes in the study of human languages, made possible by different linguistic representations. Although treatments in different linguistic traditions vary, representations at the lexical, syntactic, and semantic levels are a common feature in nearly all theoretical studies of human language, and until relatively recently, these representations are explicitly "materialized" in language processing pipelines (for example, semantic role labeling takes as input a syntactic parse).

I recently wrote a book on deep learning - Mastering PyTorch which is now available on Amazon. It is an applied deep learning book with tons of exercises on training, testing, deploying, interpreting .. various kinds of deep learning models, using PyTorch. If you want to get hands-on proficiency in deep learning, this book can be a good resource. I have tried to keep the contents easy to grasp while retaining all the essential technical concepts.If you do get a copy, please let me know how you found it, and possibly leave an Amazon review. You can also read a synopsis of the book here.

A Rice University computer science lab challenges -- and beats -- deep learning in a test to see if a new bioinformatics approach effectively tracks the lab of origin of a synthetic genetic sequence.

Deep learning is an Artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled.