In this section, we describe the details of the network architectures used in Sec. 4 and 5. We mainly used 4 GPUs (NVIDIA V100; 16GB) for the experiments in Sec. 4 and 5 and it took about 4 hours per seed (in the case of 3M steps). Actually, we conducted exhaustive evaluations through the enormous experiments, and we hope our empirical observations and recommendations help the practitioners to explore the explosive configuration space. Learning rate (policy) 1e-4 5e-5 3e-4 3e-4 Learning rate (value) 1e-4 1e-2 3e-4 3e-4 Weight initialization Uniform Xavier Uniform Xavier Uniform Xavier Uniform Initial output scale (policy) 1.0 1e-4 1e-2 1e-2 Target update Hard - Soft (5e-3) Soft (5e-3) Clipped Double Q False - True True Table 7: Details of each network architecture. We refer the original implementations of each algorithm which is available online [23, 14, 48, 27, 42].

While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often requires knowledge-grounded dialogues that are difficult to obtain. To overcome the data challenge and reduce the cost of building a knowledgegrounded dialogue system, we explore the problem under a zero-resource setting by assuming no context-knowledge-response triples are needed for training. To this end, we propose representing the knowledge that bridges a context and a response and the way that the knowledge is expressed as latent variables, and devise a variational approach that can effectively estimate a generation model from a dialogue corpus and a knowledge corpus that are independent with each other. Evaluation results on three benchmarks of knowledge-grounded dialogue generation indicate that our model can achieve comparable performance with stateof-the-art methods that rely on knowledge-grounded dialogues for training, and exhibits a good generalization ability over different topics and different datasets.

For all Reviewers: Thank you for the valuable comments that help us improve the work. Wizard) to train a knowledge-grounded generation model. GT-knowledge in the input K knowledge sentences on WoW seen and WoW unseen are 37.7% and 37.4% respectively. Finally, to speed up training, we use the number 10. CMU_DoG (pseudo supervision created by selecting GT-knowledge using Sim(.,.) with the response), and the results For REALM, the notification date of ICML 2020 is quite close to the submission date of NeurlPS 2020. For Reviewer #2: We will follow your suggestions on the improvement of clarity in the final version.

Recurrent Neural Networks (RNNs) capture long dependencies and context, and hence are the key component of typical sequential data based tasks. However, the sequential nature of RNNs dictates a large inference cost for long sequences even if the hardware supports parallelization. To induce long-term dependencies, and yet admit parallelization, we introduce novel shallow RNNs. In this architecture, the first layer splits the input sequence and runs several independent RNNs.

Despite its high search efficiency, differential architecture search (DARTS) often selects network architectures with dominated skip connections which lead to performance degradation. However, theoretical understandings on this issue remain absent, hindering the development of more advanced methods in a principled way. In this work, we solve this problem by theoretically analyzing the effects of various types of operations, e.g.

A New Jersey Mayor has shared new footage of'glowing orbs transforming into drones' over Long Island, adding more intrigue to this ongoing mystery. Michael Melham, the Mayor of Belleville, has been outspoken about the unexplained phenomena plaguing his state and the greater tri-state area since mid-November when the drones first appeared. He shared the bizarre footage on X, saying the clips'appears to show glowing orbs turning into drones. Verified not to be planes via flight tracker. In a recent interview with NewsNation, Melham said he is still getting reports of drone sightings'all over New Jersey, and even Long Island.' 'Here in New Jersey, we are about 500 mayors strong, we are still waiting for answers because our residents are still gravely concerned over what's flying just over our homes,' he said.

It supports vivid 4K Ultra HD, HDR10, HLG, and Dolby Digital Plus, includes the Alexa voice Remote Enhanced, and boasts four HDMI inputs so you can connect all your peripherals. We featured it earlier this month when it dropped to 359.99 for Prime members, but as of Jan. 24, everyone can enjoy that low price. Prime member or not, you can save 22% on this 55-inch model ahead of the Big Game. If you have limited space in your kitchen, we suggest snagging a multi-functional appliance like the Instant Pot. The six-quart Instant Pot Duo Plus is one of our favorites and it is on sale for just 69.99 -- that's 46% off its usual cost.

LinkedIn is facing a class-action lawsuit over allegations of using private messages to train its AI model. The lawsuit, filed in the U.S. District Court in the Northern District of California, has accused the Microsoft-owned professional networking site of "unlawfully disclosing its Premium customers' private messages to third parties" and "concealing" its practices by "stealthily altering its privacy policies and statements." A key part of the lawsuit accused LinkedIn of disclosing private InMail messages to third parties to train its model. A spokesperson for LinkedIn said, "we are not using member messages to train models as alleged in the complaint." The issue of attaining training data for AI models is a contentious one, and LinkedIn is not the first company to be accused of misconduct.

This is the part of the article where I usually drop a witty line to spark your curiosity or offer a fresh perspective on something intriguing. There I was, minding my own bidness and getting some work done, when I got a Slack notification from Jason (ZD's big boss). He sent me an article about Meta's plans to integrate AI-generated user profiles and content across its social media platforms. His caption: "What do you think about this?" That's corporate talk for, "I'm subtly telling you what to do, and you should write about this…" Not gonna lie, I had to check the date because it had to be April 1st. Why would Meta want to generate AI users?

Perplexity AI is going mobile with a new digital assistant for your Android phone. The company announced the feature this week, explaining that it lets you use Perplexity as usual, but takes things a step further by integrating with other apps on your phone and chaining commands -- meaning you can play media, set reminders, send texts and emails, book rides, learn about things using your camera, and more. Perplexity's phone assistant is free and doesn't require a Pro subscription. I decided to give it a try for a while and made it my default phone assistant. Just one thing is keeping me from sticking with it for good: its current lack of integration with my calendar.