Hackernoon

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

:::info Author: (1) Mingda Chen. ::: Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised … Read more

Offering an API to customers increases your revenue, but it also expands your attack surface. Businesses can offer an API that can be embedded into third-party applications to make development easier. For example, embedding social media into an application lets customers discuss a product without adding extensive overhead to your development team. The social media … Read more

:::info Authors: (1) Praveen Tirupattur,  University of Central Florida. ::: Table of Links Abstract Acknowledgements Chapter 1: Introduction Chapter 2: Related Work Chapter 3: Proposed Approach Chapter 4: Experiments and Results Chapter 5: Conclusions and Future Work Bibliography 4. Experiments and Results In this chapter, details of the experiments conducted to evaluate the performance of … Read more

:::info Authors: (1) Praveen Tirupattur,  University of Central Florida. ::: Table of Links Abstract Acknowledgements Chapter 1: Introduction Chapter 2: Related Work Chapter 3: Proposed Approach Chapter 4: Experiments and Results Chapter 5: Conclusions and Future Work Bibliography Bibliography [1] E. Acar, S. Spiegel, S. Albayrak, and D. Labor. Mediaeval 2011 affect task: Violent scene … Read more

:::info Authors: (1) Praveen Tirupattur,  University of Central Florida. ::: Table of Links Abstract Acknowledgements Chapter 1: Introduction Chapter 2: Related Work Chapter 3: Proposed Approach Chapter 4: Experiments and Results Chapter 5: Conclusions and Future Work Bibliography 3. Proposed Approach This chapter provides a detailed description of the approach followed in this work. The … Read more

:::info Authors: (1) Praveen Tirupattur,  University of Central Florida. ::: Table of Links Abstract Acknowledgements Chapter 1: Introduction Chapter 2: Related Work Chapter 3: Proposed Approach Chapter 4: Experiments and Results Chapter 5: Conclusions and Future Work Bibliography Abstract In the recent years, there has been a tremendous increase in the amount of video content … Read more

:::info Authors: (1) Praveen Tirupattur,  University of Central Florida. ::: Table of Links Abstract Acknowledgements Chapter 1: Introduction Chapter 2: Related Work Chapter 3: Proposed Approach Chapter 4: Experiments and Results Chapter 5: Conclusions and Future Work Bibliography 5. Conclusions and Future Work In this chapter, the conclusions and the directions in which the existing … Read more

:::info Authors: (1) Praveen Tirupattur,  University of Central Florida. ::: Table of Links Abstract Acknowledgements Chapter 1: Introduction Chapter 2: Related Work Chapter 3: Proposed Approach Chapter 4: Experiments and Results Chapter 5: Conclusions and Future Work Bibliography 1. Introduction The amount of multimedia content uploaded to social networking websites and the ease with which … Read more

:::info Authors: (1) Praveen Tirupattur,  University of Central Florida. ::: Table of Links Abstract Acknowledgements Chapter 1: Introduction Chapter 2: Related Work Chapter 3: Proposed Approach Chapter 4: Experiments and Results Chapter 5: Conclusions and Future Work Bibliography 2. Related Work Violence Detection is a sub-task of activity recognition where violent activities are to be … Read more

“No one is crazy… Your personal experiences with money make up maybe 0.00000001% of what’s happened in the world, but maybe 80% of how you think the world works.” GameStop and AMC rallied again. The cryptosphere’s market cap is $2.54T at the time of writing. A majority of Americans feel frustrated that the wealthy and … Read more

DUBAI,UAE, June 1st, 2024/Chainwire/–The Penguiana project, a penguin-themed meme coin built on the Solana blockchain, has achieved a remarkable milestone, hitting an all-time high fully diluted valuation of $4 Million and currently sitting at a market cap of over $2.5 million, according to Dexscreener. This significant growth underscores the strong confidence and community enthusiasm as … Read more

:::info Author: (1) Hiroki Takizawa, Preferred Networks, Inc., Chiyoda-ku, Tokyo, Japan (contact@hiroki-takizawa.name). ::: Table of Links Abstract and Intro Related Works Methods Results Discussion and Conclusions and Acknowledgements Additional Information and Declarations and References Additional Information and Declarations Competing Interests The author declares that there are no competing interests. Author Contributions Hiroki Takizawa conceived and … Read more

:::info Author: (1) Hiroki Takizawa, Preferred Networks, Inc., Chiyoda-ku, Tokyo, Japan (contact@hiroki-takizawa.name). ::: Table of Links Abstract and Intro Related Works Methods Results Discussion and Conclusions and Acknowledgements Additional Information and Declarations and References 5 Discussion and Conclusions We conclude that our study has weakly solved Othello, although we recognize that our achievement is just … Read more

:::info Author: (1) Hiroki Takizawa, Preferred Networks, Inc., Chiyoda-ku, Tokyo, Japan (contact@hiroki-takizawa.name). ::: Table of Links Abstract and Intro Related Works Methods Results Discussion and Conclusions and Acknowledgements Additional Information and Declarations and References 4 Results First of all, we enumerated and shortly evaluated all positions with 50 empty squares. We only enumerated positions with … Read more

:::info Author: (1) Hiroki Takizawa, Preferred Networks, Inc., Chiyoda-ku, Tokyo, Japan (contact@hiroki-takizawa.name). ::: Table of Links Abstract and Intro Related Works Methods Results Discussion and Conclusions and Acknowledgements Additional Information and Declarations and References 3 Methods 3.1 Use of Terms “Ply” and “Move” In chess, there is a tradition where two sequential moves, one from … Read more

:::info Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: gladys.tyen@cl.cam.ac.uk); (2) Hassan Mansoor, Google Research (e-mail: hassan@google.com); (3) Victor Carbune, Google Research (e-mail: vcarbune@google.com); (4) Peter Chen, Google Research and Equal leadership contribution (chenfeif@google.com); (5) Tony Mak, Google … Read more

:::info Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: gladys.tyen@cl.cam.ac.uk); (2) Hassan Mansoor, Google Research (e-mail: hassan@google.com); (3) Victor Carbune, Google Research (e-mail: vcarbune@google.com); (4) Peter Chen, Google Research and Equal leadership contribution (chenfeif@google.com); (5) Tony Mak, Google … Read more

:::info Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: gladys.tyen@cl.cam.ac.uk); (2) Hassan Mansoor, Google Research (e-mail: hassan@google.com); (3) Victor Carbune, Google Research (e-mail: vcarbune@google.com); (4) Peter Chen, Google Research and Equal leadership contribution (chenfeif@google.com); (5) Tony Mak, Google … Read more

:::info Authors: (1) Gladys Tyen, University of Cambridge, Dept. of Computer Science & Technology, ALTA Institute, and Work done during an internship at Google Research (e-mail: gladys.tyen@cl.cam.ac.uk); (2) Hassan Mansoor, Google Research (e-mail: hassan@google.com); (3) Victor Carbune, Google Research (e-mail: vcarbune@google.com); (4) Peter Chen, Google Research and Equal leadership contribution (chenfeif@google.com); (5) Tony Mak, Google … Read more

The time traveler Reddit post is an inextricable part of Bitcoin lore. The text, which supposedly comes from the year 2025, is famous for introducing “The Citadel” concept to the ecosystem. In general, it predicts Bitcoin taking over and the unimaginable horrors caused by the population refusing to spend it because, without inflation, they have … Read more

Ockam teamed up with Redpanda to launch Redpanda Connect with Ockam: the first zero-trust streaming data platform. This is a natural partnership because both companies have the same ethos; to enable every developer to build distributed systems, at scale, with simple tools. Both companies’ products are based on popular open-source projects and are built by veteran, high-performing teams. … Read more

Python provides many useful libraries and functions to work with strings. Sometimes, you may need to split a string at every Nth character. It can be tedious to loop through the string and extract substrings, one at a time. In this article, we will learn three simple ways to quickly split a string into substrings … Read more