We have seen recent advances in the fields of Machine Learning (ML), Deep Learning (DL), and Artificial intelligence (AI) that the models are becoming increasingly complex and large in terms of architecture and parameter size. These complex ML/DL models have beaten the state of the art in most fields of computer science like computer vision, NLP, tabular data prediction and time series forecasting, etc. With the increase in models’ performance, model explainability and interpretability has become essential to explain/justify model outcome, especially for business use cases. There has been significant improvement in the domain of model explainability for Computer Vision and Natural Language Processing (NLP) tasks with fundamental research for both black-box and white-box techniques. In this paper, we proposed novel time series explainability techniques SPASE for black-box time series model forecasting and anomaly detection problems.

Machine Learning has seen tremendous growth recently, which has led to a larger adaptation of ML systems for educational assessments, credit risk, healthcare, employment, criminal justice, to name a few. The trustworthiness of ML and NLP systems is a crucial aspect and requires a guarantee that the decisions they make are fair and robust. Aligned with this, we propose a novel framework GYC, to generate a set of exhaustive counterfactual text, which are crucial for testing these ML systems. Our main contributions include a) We introduce GYC, a framework to generate counterfactual samples such that the generation is plausible, diverse, goal-oriented, and effective, b) We generate counterfactual samples, that can direct the generation towards a corresponding \texttt{condition} such as named-entity tag, semantic role label, or sentiment. Our experimental results on various domains show that GYC generates counterfactual text samples exhibiting the above four properties. GYC generates counterfactuals that can act as test cases to evaluate a model and any text debiasing algorithm.

The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models, there are limited efforts towards improving the data quality.

With an abundance of research papers in deep learning, adoption and reproducibility of existing works becomes a challenge. To make a DL developer life easy, we propose a novel system, DARVIZ, to visually design a DL model using a drag-and-drop framework in an platform agnostic manner. The code could be automatically generated in both Caffe and Keras. DARVIZ could import (i) any existing Caffe code, or (ii) a research paper containing a DL design; extract the design, and present it in visual editor.

With an abundance of research papers in deep learning, reproducibility or adoption of the existing works becomes a challenge. This is due to the lack of open source implementations provided by the authors. Even if the source code is available, then re-implementing research papers in a different library is a daunting task. To address these challenges, we propose a novel extensible approach, DLPaper2Code, to extract and understand deep learning design flow diagrams and tables available in a research paper and convert them to an abstract computational graph. The extracted computational graph is then converted into execution ready source code in both Keras and Caffe, in real-time. An arXiv-like website is created where the automatically generated designs is made publicly available for 5,000 research papers. The generated designs could be rated and edited using an intuitive drag-and-drop UI framework in a crowd sourced manner. To evaluate our approach, we create a simulated dataset with over 216,000 valid deep learning design flow diagrams using a manually defined grammar. Experiments on the simulated dataset show that the proposed framework provide more than 93% accuracy in flow diagram content extraction.

In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi.Sandhi splitting is the process of splitting a given compound word into its constituent morphemes.Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word.Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits.In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95% accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5% accuracy, outperforming the state-of-art by 20%.Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well.