The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare. The detection and mitigation of biased models is a very delicate task which should be tackled with care and involving domain experts in the loop. In this paper we introduce FairLens, a methodology for discovering and explaining biases. We show how our tool can be used to audit a fictional commercial black-box model acting as a clinical decision support system. In this scenario, the healthcare facility experts can use FairLens on their own historical data to discover the model's biases before incorporating it into the clinical decision flow. FairLens first stratifies the available patient data according to attributes such as age, ethnicity, gender and insurance; it then assesses the model performance on such subgroups of patients identifying those in need of expert evaluation. Finally, building on recent state-of-the-art XAI (eXplainable Artificial Intelligence) techniques, FairLens explains which elements in patients' clinical history drive the model error in the selected subgroup. Therefore, FairLens allows experts to investigate whether to trust the model and to spotlight group-specific biases that might constitute potential fairness issues.

The deep neural networks (DNNs) have achieved great success in learning complex patterns with strong predictive power, but they are often thought of as "black box" models without a sufficient level of transparency and interpretability. It is important to demystify the DNNs with rigorous mathematics and practical tools, especially when they are used for mission-critical applications. This paper aims to unwrap the black box of deep ReLU networks through local linear representation, which utilizes the activation pattern and disentangles the complex network into an equivalent set of local linear models (LLMs). We develop a convenient LLM-based toolkit for interpretability, diagnostics, and simplification of a pre-trained deep ReLU network. We propose the local linear profile plot and other visualization methods for interpretation and diagnostics, and an effective merging strategy for network simplification. The proposed methods are demonstrated by simulation examples, benchmark datasets, and a real case study in home lending credit risk assessment.

Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strengthened this hypothesis, the resulting diversity of model-based methods has also made it difficult to track which components drive success and why. In this paper, we seek to disentangle the contributions of recent methods by focusing on three questions: (1) How does planning benefit MBRL agents? (2) Within planning, what choices drive performance? (3) To what extent does planning improve generalization? To answer these questions, we study the performance of MuZero (Schrittwieser et al., 2019), a state-of-the-art MBRL algorithm, under a number of interventions and ablations and across a wide range of environments including control tasks, Atari, and 9x9 Go. Our results suggest the following: (1) The primary benefit of planning is in driving policy learning. (2) Using shallow trees with simple Monte-Carlo rollouts is as performant as more complex methods, except in the most difficult reasoning tasks. (3) Planning alone is insufficient to drive strong generalization. These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.

Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, Long-Range Arena, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. Long-Range Arena paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. Transformers (Vaswani et al., 2017) are ubiquitously state-of-the-art across many modalities, from language (Devlin et al., 2018; Raffel et al., 2019; Child et al., 2019) to images (Tan & Bansal, 2019; Lu et al., 2019) to protein sequences (Rives et al., 2019). A common weakness of Transformers is their quadratic memory complexity within the self-attention mechanism that restricts their potential application to domains requiring longer sequence lengths. To date, a dizzying number of efficient Transformer models ('xformers') have been proposed to tackle this problem (Liu et al., 2018; Kitaev et al., 2020; Wang et al., 2020; Tay et al., 2020b; Katharopoulos et al., 2020). Many of these models demonstrate comparable performance to the vanilla Transformer model while successfully reducing the memory complexity of the self-attention mechanism. An overview of this research area can be found in (Tay et al., 2020c).

Human use language not just to convey information but also to express their inner feelings and mental states. In this work, we adapt the state-of-the-art language generation models to generate affective (emotional) text. We posit a model capable of generating affect-driven and topic focused sentences without losing grammatical correctness as the affect intensity increases. We propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2. The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text. Previous attempts at modelling fine-grained emotions fall out on grammatical correctness at extreme intensities, but our model is resilient to this and delivers robust results at all intensities. We conduct automated evaluations and human studies to test the performance of our model, and provide a detailed comparison of the results with other models. In all evaluations, our model outperforms existing affective text generation models.

There has recently been increasing interest in learning representations of temporal knowledge graphs (KGs), which record the dynamic relationships between entities over time. Temporal KGs often exhibit multiple simultaneous non-Euclidean structures, such as hierarchical and cyclic structures. However, existing embedding approaches for temporal KGs typically learn entity representations and their dynamic evolution in the Euclidean space, which might not capture such intrinsic structures very well. To this end, we propose Dy- ERNIE, a non-Euclidean embedding approach that learns evolving entity representations in a product of Riemannian manifolds, where the composed spaces are estimated from the sectional curvatures of underlying data. Product manifolds enable our approach to better reflect a wide variety of geometric structures on temporal KGs. Besides, to capture the evolutionary dynamics of temporal KGs, we let the entity representations evolve according to a velocity vector defined in the tangent space at each timestamp. We analyze in detail the contribution of geometric spaces to representation learning of temporal KGs and evaluate our model on temporal knowledge graph completion tasks. Extensive experiments on three real-world datasets demonstrate significantly improved performance, indicating that the dynamics of multi-relational graph data can be more properly modeled by the evolution of embeddings on Riemannian manifolds.

Information technology (IT) students in Denmark have created a software program that can determine the energy consumption and the amount of carbon dioxide generated by the development of deep learning algorithms. According to their estimates, hardware used to train a deep learning algorithm can use worrying amounts of energy from an environmental standpoint. Whether browsing movies suggested by Netflix based on your viewing history, asking your voice assistant a question or interacting with a chatbot on an e-commerce website, all of these everyday online processes rely on deep learning algorithms. However, developing algorithms contributes to digital pollution. And it's precisely this environmental impact that students from the IT department of the University of Copenhagen have sought to quantify, using their Carbontracker software program.

Sigmoid and tanh were monotonous, differentiable, and previously more popular activation functions. However, these functions suffer saturation over time, and this leads to problems occurring with vanishing gradients. An alternative and the most popular activation function to overcome this issue is the Rectified Linear Unit (ReLU). The above diagram with the blue line is the representation of the Rectified Linear Unit (ReLU) whereas, the green line is a variant of ReLU called Softplus. The other variants of ReLU include Leaky ReLU, ELU, SiLU, etc., which are used for better performance in some tasks.

Artificial Intelligence (AI) has the potential to transform how we fight climate change. However, it also increasingly contributes to it: the carbon footprint of AI will grow exponentially over the next decade, and is projected to grow at a CAGR of nearly 44% globally through 2025. The industry is trending towards bigger models (e.g. GPT-3): these require ever-growing datasets, compute budgets, and incur massive energy bills over the model lifecycle. Computational costs of AI models have been doubling every few months, resulting in an estimated 300,000x increase from 2012-2018.

The calculation of curve length is one of the most major components in many modern and classical problem. For example, a handwritten signature involves the computation of the length along the curve (Ooi et al.). When one handles the challenge of length computation in real-life problems he faces several constraints such as additive noise, discretization error, and even partial information. In this work, we address a fundamental question in the field of geometry where we aim to reconstruct a basic property using DNN. The simplest geometric object is a curve, and a simple metric to evaluate a curve is the length.