While deep learning-based classification is generally addressed using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pair (x,y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences often lack a natural probabilistic meaning. We address these issues by proposing Deep Conditional Target Densities (DCTD), a novel and general regression method with a clear probabilistic interpretation. DCTD models the conditional target density p (y x) by using a neural network to directly predict the un-normalized density from (x,y). This model of p (y x) is trained by minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our approach outperforms direct regression, as well as other probabilistic and confidence-based methods. Notably, our regression model achieves a 1.9% AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-of- the-art on visual tracking when applied for bounding box regression. Supervised regression entails learning a model capable of predicting a continuous target value y from an input x, given a set of paired training examples. It is a fundamental machine learning problem with many important applications within computer vision and other domains. While all of these tasks benefit from accurate regression of the target values, high accuracy can even be safety-critical in e.g. automotive and medical applications. Today, such regression problems are commonly tackled using Deep Neural Networks (DNNs), due to their ability to learn powerful feature representations from data. While classification is generally addressed using standardized losses and output representations, a wide variety of different techniques are employed for regression. The most conventional strategy is to train a DNN to directly predict a target y given an input x (Lathuili ere et al., 2019). The training data { (x i,y i)} 2000 i 1 is generated by the ground truth conditional target density p (y x) . DCTD models p (y x) by directly predicting the un-normalized density from the input-target pair (x,y), and is trained by minimizing the associated negative log-likelihood.

Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs. We show how to overcome the limitations of intensity-based approaches by directly modeling the conditional distribution of inter-event times. We draw on the literature on normalizing flows to design models that are flexible and efficient. We additionally propose a simple mixture model that matches the flexibility of flow-based models, but also permits sampling and computing moments in closed form. The proposed models achieve state-of-the-art performance in standard prediction tasks and are suitable for novel applications, such as learning sequence embeddings and imputing missing data.

A long-standing challenge in reinforcement learning is the design of function approximations and efficient learning algorithms that provide agents with fast training, robust learning , and high performance in complex environments. To this end, the use of prior knowledge, while promising, is often costly and, in essence, challenging to scale up. In contrast, we consider problem knowledge signals, that are any relevant indicator useful to solve a task, e.g., metrics of uncertainty or proactive prediction of future states. Our framework consists of predicting such complementary quantities associated with self-performance assessment and accurate expectations. Therefore, policy and value functions are no longer only optimized for a reward but are learned using environment-agnostic quantities. We propose a generally applicable framework for structuring reinforcement learning by injecting problem knowledge in policy gradient updates. In this paper: (a) We introduce MERL, our multi-head reinforcement learning framework for generalized auxiliary tasks. (b) We conduct experiments across a variety of standard benchmark environments. Our results show that MERL improves performance for on-and off-policy methods. (c) We show that MERL also improves transfer learning on a set of challenging tasks. (d) We investigate how our approach addresses the problem of reward sparsity and pushes the function approximations into a better-constrained parameter configuration.

A BSTRACT Current deep neural networks can achieve remarkable performance on a single task. However, when the deep neural network is continually trained on a sequence of tasks, it seems to gradually forget the previous learned knowledge. This phenomenon is referred to as catastrophic forgetting and motivates the field called lifelong learning. The central question in lifelong learning is how to enable deep neural networks to maintain performance on old tasks while learning a new task. In this paper, we introduce a novel and effective lifelong learning algorithm, called MixEd stochastic GrAdient (MEGA), which allows deep neural networks to acquire the ability of retaining performance on old tasks while learning new tasks. Extensive experimental results show that the proposed MEGA algorithm significantly advances the state-of-the-art on all four commonly used lifelong learning benchmarks, reducing the error by up to 18%. 1 I NTRODUCTION A significant step towards artificial general intelligence (AGI) is to enable the learning agent to acquire the ability of remembering past experiences while being trained on a continuum of tasks. Current deep neural networks are capable of achieving remarkable performance on a single task (Goodfellow et al., 2016). However when the network is retrained on a new task, its performance drops drastically on previously trained tasks, a phenomenon which is referred to as catastrophic forgetting (Ratcliff, 1990; Robins, 1995; French, 1999; Kirkpatrick et al., 2017).

Meta-learning has been widely used for implementing few-shot learning and fast model adaptation. One kind of meta-learning methods attempt to learn how to control the gradient descent process in order to make the gradient-based learning have high speed and generalization. This work proposes a method that controls the gradient descent process of the model parameters of a neural network by limiting the model parameters in a low-dimensional latent space. The main challenge of this idea is that a decoder with too many parameters is required. This work designs a decoder with typical structure and shares a part of weights in the decoder to reduce the number of the required parameters. Besides, this work has introduced ensemble learning to work with the proposed approach for improving performance. The results show that the proposed approach is witnessed by the superior performance over the Omniglot classification and the miniImageNet classification tasks.

Domain shifts are frequently encountered in real-world scenarios. In this paper, we consider the problem of domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN). By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of basis elements, we show for the first time, both empirically and theoretically, that domain shifts can be effectively handled by decomposing a regular convolutional layer into a domain-specific basis layer and a domain-shared basis coefficient layer, while both remain convolutional. An input channel will now first convolve spatially only with each respective domain-specific basis to "absorb" domain variations, and then output channels are linearly combined using common basis coefficients trained to promote shared semantics across domains. We use toy examples, rigorous analysis, and real-world examples to show the framework's effectiveness in cross-domain performance and domain adaptation. With the proposed architecture, we need only a small set of basis elements to model each additional domain, which brings a negligible amount of additional parameters, typically a few hundred. 1 Introduction Training supervised deep networks requires large amount of labeled training data; however, well-trained deep networks often degrade dramatically on testing data from a significantly different domain. In real-world scenarios, such domain shifts are introduced by many factors, such as different illumination, viewing angles, and resolutions. Research topics such as transfer learning and domain adaptation are studied to promote invariant representations across domains with different levels of availabilities of annotated data. Recent efforts on learning cross-domain invariant representations using deep networks generally fall into two categories. The first one is to learn a common network with constraints encouraging invariant feature representations across different domains [11, 14, 26].

The MIT Stephen A. Schwarzman College of Computing (SCC) will reorient the Institute to bring the power of computing and artificial intelligence to all fields at MIT, and to allow the future of computing and AI to be shaped by all MIT disciplines. To support ongoing planning for the new college, Dean Melissa Nobles invited faculty from all 14 of MIT's humanistic disciplines in the School of Humanities, Arts, and Social Sciences to respond to two questions: As Nobles says in her foreword to the series, "Together, the following responses to these two questions offer something of a guidebook to the myriad, productive ways that technical, humanistic, and scientific fields can join forces at MIT, and elsewhere, to further human and planetary well-being." The following excerpts highlight faculty responses, with links to full commentaries. The excerpts are sequenced by fields in the following order: the humanities, arts, and social sciences. "The advent of artificial intelligence presents our species with an historic opportunity -- disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world? With a sense of promise and urgency, we are embarked at MIT on an accelerated effort to more fully integrate the technical and humanistic forms of discovery in our curriculum and research, and in our habits of mind and action."

Artificial Intelligence (AI) is no sooner the next big thing, it is now a big technology in digital marketing. All digital marketing operations are now affected by AI-powered devices. From startups to large companies are opting for AI-powered digital marketing equipments to enhance campaign planning and decision making. AI-based tools are now a flourishing market, with an exceptional change demand. As per the greater part of the digital marketers AI upgrading every one of the zones where the prescient investigation, basic leadership and mechanization endeavors required.

Today, education doesn't stop after students graduate; many continue learning throughout their life. And in addition to adult learning courses at colleges and universities, a lot of courses are now offered online – ranging from MOOCs ("massive open online course") to many apps. At the Global Education & Skills Forum in March, two of the 10 finalists were lifelong learning startups. Here are four of the most promising EdTech startups around. Don't have time to read a book a day?

Voice-controlled personal and home assistants (such as the Amazon Echo and Apple Siri) are becoming increasingly popular for a variety of applications. However, the benefits of these technologies are not readily accessible to Deaf or Hard-ofHearing (DHH) users. The objective of this study is to develop and evaluate a sign recognition system using multiple modalities that can be used by DHH signers to interact with voice-controlled devices. With the advancement of depth sensors, skeletal data is used for applications like video analysis and activity recognition. Despite having similarity with the well-studied human activity recognition, the use of 3D skeleton data in sign language recognition is rare. This is because unlike activity recognition, sign language is mostly dependent on hand shape pattern. In this work, we investigate the feasibility of using skeletal and RGB video data for sign language recognition using a combination of different deep learning architectures. We validate our results on a large-scale American Sign Language (ASL) dataset of 12 users and 13107 samples across 51 signs. It is named as GMUASL51. We collected the dataset over 6 months and it will be publicly released in the hope of spurring further machine learning research towards providing improved accessibility for digital assistants.