Modern neural networks are often quite wide, causing large memory and computation costs. It is thus of great interest to train a narrower network. However, training narrow neural nets remains a challenging task. We ask two theoretical questions: Can narrow networks have as strong expressivity as wide ones? If so, does the loss function exhibit a benign optimization landscape?

As learned control policies become increasingly common in autonomous systems, there is increasing need to ensure that they are interpretable and can be checked by human stakeholders. Formal specifications have been proposed as ways to produce human-interpretable policies for autonomous systems that can still be learned from examples. Previous work showed that despite claims of interpretability, humans are unable to use formal specifications presented in a variety of ways to validate even simple robot behaviors. This work uses active learning, a standard pedagogical method, to attempt to improve humans' ability to validate policies in signal temporal logic (STL). Results show that overall validation accuracy is not high, at 65\% \pm 15% (mean \pm standard deviation), and that the three conditions of no active learning, active learning, and active learning with feedback do not significantly differ from each other.

Shape assembly is a ubiquitous task in daily life, integral for constructing complex 3D structures like IKEA furniture. While significant progress has been made in developing autonomous agents for shape assembly, existing datasets have not yet tackled the 4D grounding of assembly instructions in videos, essential for a holistic understanding of assembly in 3D space over time. We introduce IKEA Video Manuals, a dataset that features 3D models of furniture parts, instructional manuals, assembly videos from the Internet, and most importantly, annotations of dense spatio-temporal alignments between these data modalities. To demonstrate the utility of IKEA Video Manuals, we present five applications essential for shape assembly: assembly plan generation, part-conditioned segmentation, part-conditioned pose estimation, video object segmentation, and furniture assembly based on instructional video manuals. For each application, we provide evaluation metrics and baseline methods.

It provides problem-related (asymptotic) lower and upper bounds on the regret, the latter for an algorithm presented in the paper that builds on Burnetas and Katehakis (1997) and a recent bandit paper by Combes et al (NIPS 2017). The setting assumes that an "MDP structure" \Phi (i.e. a set of possible MDP models) is given. The regret bounds (after T steps) are shown to be of the form K_Phi*log T, where the parameter K_\Phi is the solution to a particular optimization problem. It is shown that if \Phi is the set of all MDPs ("the unstructured case") then K_\Phi is bounded by HSA/\delta, where H is the bias span and \delta the minimal action sub-optimality gap. The second particular class that is considered is the Lipschitz structure that considers embeddings of finite MDPs in Euclidian space such that transition probabilities and rewards are Lipschitz. In this case, the regret bounds are shown to not to depend on the size of state and action space anymore.

Active learning aims to construct an effective training set by iteratively curating the most informative unlabeled data for annotation, which is practical in low-resource tasks. Most active learning techniques in classification rely on the model's uncertainty or disagreement to choose unlabeled data. However, previous work indicates that existing models are poor at quantifying predictive uncertainty, which can lead to over-confidence in superficial patterns and a lack of exploration. Inspired by the cognitive processes in which humans deduce and predict through causal information, we propose a novel Explainable Active Learning framework (XAL) for low-resource text classification, which aims to encourage classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Specifically, besides using a pre-trained bi-directional encoder for classification, we employ a pre-trained uni-directional decoder to generate and score the explanation. A ranking loss is proposed to enhance the decoder's capability in scoring explanations. During the selection of unlabeled data, we combine the predictive uncertainty of the encoder and the explanation score of the decoder to acquire informative data for annotation. As XAL is a general framework for text classification, we test our methods on six different classification tasks. Extensive experiments show that XAL achieves substantial improvement on all six tasks over previous AL methods. Ablation studies demonstrate the effectiveness of each component, and human evaluation shows that the model trained in XAL performs surprisingly well in explaining its prediction.

Viral Video: It has been a long time since we started availing the services of robots, the electronic humans, perhaps the first ever non-official definition of the wonder machine. Now, robotics is very much in vogue and has mass use across industries. One of the key reasons to deploy these programmable machines is their high efficiency and the ability to work for longer hours than humans without getting tired. However, a video has surfaced showing a robot placing plastic containers on a conveyor belt. The video is in a time-lapse, suggesting that the robot has been on the job for hours and the last few frames show the real-time where the machine picks up a container and as soon as it lifts it, it collapses.

It might have been the tiny middle-aged man I matched with on Hinge who tried to lure me into his very short arms by telling me a well-rehearsed, technically touching story about the cancer charity he set up for his dead wife. Or it may have been the (indefinitely benched) Premier League player who picked me up in a leased Maserati which no part of my skin was allowed to touch. Or perhaps it was the guy who brought his laminated CV to a Brixton cocktail bar and tapped his finger on the Oxford University entry for an hour (I had, prematurely, ordered chicken wings I felt unable to abandon). Quite possibly, it was all of them and others combined. But in any case, after years of calamitous dates with random strangers that sounded fun enough but face to face made me want to remove my insides and wash them, I snapped and vowed to never search the web for love again.

Comcast is one of the email service providers and you can log-in by using credentials such as username and password. However, while using it, many users face Comcast email, not working problems. Here we discussed How To Resolve Xfinity Comcast Email Not Working Problems easily by going through the steps. This article helps you to get rid of several issues associated with Xfinity Comcast Email Not Working Issues. Many of them might be first-time users of Comcast.

Could a chatbot write a script for this podcast? And could it do a better job of it? Welcome to our current affairs podcast, where we delve into the world of artificial intelligence and the latest developments in the field. Today, we are focusing on a tool called ChatGPT, which has been making waves in the AI community. ChatGPT is a state-of-the-art AI language model that has the ability to generate human-like text responses in real-time.

Work with the platform development team to assist with the data collection, processing and validation. This may include software development tasks to enable the linkage of mHealth data with other health and non-health datasets. Work with the platform development team to assist with the data collection, processing and validation. This may include software development tasks to enable the linkage of mHealth data with other health and non-health datasets.