High-definition map (HD map) construction is a crucial problem for autonomous driving. This problem typically involves collecting high-quality point clouds, fusing multiple point clouds of the same scene, annotating map elements, and updating maps constantly. This pipeline, however, requires a vast amount of human efforts and resources which limits its scalability. Additionally, traditional HD maps are coupled with centimeter-level accurate localization which is unreliable in many scenarios. In this paper, we argue that online map learning, which dynamically constructs the HD maps based on local sensor observations, is a more scalable way to provide semantic and geometry priors to self-driving vehicles than traditional pre-annotated HD maps. Meanwhile, we introduce an online map learning method, titled HDMapNet. It encodes image features from surrounding cameras and/or point clouds from LiDAR, and predicts vectorized map elements in the bird's-eye view. We benchmark HDMapNet on the nuScenes dataset and show that in all settings, it performs better than baseline methods. Of note, our fusion-based HDMapNet outperforms existing methods by more than 50% in all metrics. To accelerate future research, we develop customized metrics to evaluate map learning performance, including both semantic-level and instance-level ones. By introducing this method and metrics, we invite the community to study this novel map learning problem. We will release our code and evaluation kit to facilitate future development.

This paper introduces kernel continual learning, a simple but effective variant of continual learning that leverages the non-parametric nature of kernel methods to tackle catastrophic forgetting. We deploy an episodic memory unit that stores a subset of samples for each task to learn task-specific classifiers based on kernel ridge regression. This does not require memory replay and systematically avoids task interference in the classifiers. We further introduce variational random features to learn a data-driven kernel for each task. To do so, we formulate kernel continual learning as a variational inference problem, where a random Fourier basis is incorporated as the latent variable. The variational posterior distribution over the random Fourier basis is inferred from the coreset of each task. In this way, we are able to generate more informative kernels specific to each task, and, more importantly, the coreset size can be reduced to achieve more compact memory, resulting in more efficient continual learning based on episodic memory. Extensive evaluation on four benchmarks demonstrates the effectiveness and promise of kernels for continual learning.

Jui Khankari, a 17-year-old Hinsdale Central High School student, founded AInspire, to teach the applications of artificial intelligence, or AI, to students …

Investments in housing, transit and job training emerged as top priorities for the Bay State as it recovers from the pandemic, according to a future of work report released by the Baker Administration. "The changing ways of working may shift what we think of as the'center of gravity' here in Massachusetts away from the urban core and toward the rest of the state," Baker said at a Tuesday morning press conference at the Tufts Launchpad location for BioLabs in Boston, a recipient of a Baker administration Workforce Training Fund Program grant. The report estimates that Massachusetts will need to produce 125,000 to 200,000 housing units by 2030, a $1 billion investment, with a focus on aiding homeownership among communities of color. Baker also announced $240 million in funding for workforce training programs. The report says up to 400,000 people may need to change occupations over the next decade to keep up with workplace trends.

They can be used to decide everything from which video we're recommended to watch next on YouTube to who should be arrested based on facial recognition software. But these algorithms, and the data used to train them, often replicate the harmful social biases of the engineers who build them. Eliminating this bias from technology is the focus of Code Shift, a new data science lab at Texas A&M University that brings together faculty members and researchers from a variety of disciplines across campus. It's an increasingly critical initiative, said Lab Director Srividya Ramasubramanian, as more of the world becomes automated. Machines, rather than humans, are making many of the decisions around us, including some that are high-risk.

Incorporating deep learning technology in their product portfolio to gain a competitive advantage over other competitors. The deep learning approaches have generated high-performance results when applied to NLP. As deep learning models can easily be trained with a single end-to-end mode, they do not require traditional engineering features and can help businesses. Deep learning can enhance several features of NLP, such as sentiment analysis, which can help companies gain insights into the feelings of customers, thereby enabling them to provide enhanced services to customers and predict the customers' behavior. Deep Learning Courses for NLP Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors in Deep Learning Courses for NLP Industry.

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Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL problem necessitates new approaches to generalization beyond the well-studied techniques used in supervised learning. While supervised learning methods can generalize effectively without explicitly accounting for epistemic uncertainty, we show that, perhaps surprisingly, this is not the case in RL. We show that generalization to unseen test conditions from a limited number of training conditions induces implicit partial observability, effectively turning even fully-observed MDPs into POMDPs. Informed by this observation, we recast the problem of generalization in RL as solving the induced partially observed Markov decision process, which we call the epistemic POMDP. We demonstrate the failure modes of algorithms that do not appropriately handle this partial observability, and suggest a simple ensemble-based technique for approximately solving the partially observed problem. Empirically, we demonstrate that our simple algorithm derived from the epistemic POMDP achieves significant gains in generalization over current methods on the Procgen benchmark suite.