Hackernoon

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

:::info Author: (1) Yitang Zhang. ::: Table of Links Abstract & Introduction Notation and outline of the proof The set Ψ1 Zeros of L(s, ψ)L(s, χψ) in Ω Some analytic lemmas Approximate formula for L(s, ψ) Mean value formula I Evaluation of Ξ11 Evaluation of Ξ12 Proof of Proposition 2.4 Proof of Proposition 2.6 Evaluation … Read more

TL;DR You can automatically solve typical Natural Language Processing tasks (classification, sentiment analysis, etc.) for your text data using LLM for as cheap as $10 per 1M rows (it depends on the task and the model), staying in your dbt environment. Instructions, details, and code are below If you are using dbt as your transformation … Read more

Java is one of the world’s most well-known and widely used programming languages. Since its creation in 1995, it has maintained its popularity and significance despite the rapid development of technology and the emergence of new programming languages. Java is used to develop many mobile applications for large enterprise systems. Modern Capabilities of Java With … Read more

:::info Authors: (1) Kexun Zhang, UC Santa Barbara and Equal contribution; (2) Hongqiao Chen, Northwood High School and Equal contribution; (3) Lei Li, Carnegie Mellon University; (4) William Yang Wang,UC Santa Barbara. ::: Table of Links Abstract and Intro Related Work ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec … Read more

:::info Authors: (1) Kexun Zhang, UC Santa Barbara and Equal contribution; (2) Hongqiao Chen, Northwood High School and Equal contribution; (3) Lei Li, Carnegie Mellon University; (4) William Yang Wang,UC Santa Barbara. ::: Table of Links Abstract and Intro Related Work ToolDec: LLM Tool Use via Finite-State Decoding Experiment: ToolDec Eliminates Syntax Errors Experiment: ToolDec … 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

How are you, hacker? 🪐What’s happening in tech this week: The Noonification by HackerNoon has got you covered with fresh content from our top 5 stories of the day, every day at noon your local time! Set email preference here. ## AI Is Everywhere — So Wheres the Funding? By @starinin [ 6 Min read … 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