Super tickets in pre trained language modelsHyunKyu Jeon
This document discusses finding "super tickets" in pre-trained language models through pruning attention heads and feedforward layers. It shows that lightly pruning BERT models can improve generalization without degrading accuracy (phase transition phenomenon). The authors propose a new pruning approach for multi-task fine-tuning of language models called "ticket sharing" where pruned weights are shared across tasks. Experiments on GLUE benchmarks show their proposed super ticket and ticket sharing methods consistently outperform unpruned baselines, with more significant gains on smaller tasks. Analysis indicates pruning reduces model variance and some tasks share more task-specific knowledge than others.
Synthesizer rethinking self-attention for transformer models HyunKyu Jeon
The document expresses gratitude to the reader for taking the time to listen. It does not provide any other details, context, or information beyond thanking the reader for listening. The summary captures the essence of the document in a single concise sentence.
This document summarizes Meta Back-Translation, a method for improving back-translation by training the backward model to directly optimize the performance of the forward model during training. The key points are:
1. Back-translation typically relies on a fixed backward model, which can lead the forward model to overfit to its outputs. Meta back-translation instead continually trains the backward model to generate pseudo-parallel data that improves the forward model.
2. Experiments show Meta back-translation generates translations with fewer pathological outputs like greatly differing in length from references. It also avoids both overfitting and underfitting of the forward model by flexibly controlling the diversity of pseudo-parallel data.
3. Related work leverages mon
Maxmin qlearning controlling the estimation bias of qlearningHyunKyu Jeon
This document summarizes the Maxmin Q-learning paper published at ICLR 2020. Maxmin Q-learning aims to address the overestimation bias of Q-learning and underestimation bias of Double Q-learning by maintaining multiple Q-functions and using the minimum value across them for the target in the Q-learning update. It defines the action selection and target construction for the update based on taking the maximum over the minimum Q-value for each action. The algorithm initializes multiple Q-functions, selects a random subset to update using the maxmin target constructed from the minimum Q-values. This approach reduces the biases seen in prior methods.
Super tickets in pre trained language modelsHyunKyu Jeon
This document discusses finding "super tickets" in pre-trained language models through pruning attention heads and feedforward layers. It shows that lightly pruning BERT models can improve generalization without degrading accuracy (phase transition phenomenon). The authors propose a new pruning approach for multi-task fine-tuning of language models called "ticket sharing" where pruned weights are shared across tasks. Experiments on GLUE benchmarks show their proposed super ticket and ticket sharing methods consistently outperform unpruned baselines, with more significant gains on smaller tasks. Analysis indicates pruning reduces model variance and some tasks share more task-specific knowledge than others.
Synthesizer rethinking self-attention for transformer models HyunKyu Jeon
The document expresses gratitude to the reader for taking the time to listen. It does not provide any other details, context, or information beyond thanking the reader for listening. The summary captures the essence of the document in a single concise sentence.
This document summarizes Meta Back-Translation, a method for improving back-translation by training the backward model to directly optimize the performance of the forward model during training. The key points are:
1. Back-translation typically relies on a fixed backward model, which can lead the forward model to overfit to its outputs. Meta back-translation instead continually trains the backward model to generate pseudo-parallel data that improves the forward model.
2. Experiments show Meta back-translation generates translations with fewer pathological outputs like greatly differing in length from references. It also avoids both overfitting and underfitting of the forward model by flexibly controlling the diversity of pseudo-parallel data.
3. Related work leverages mon
Maxmin qlearning controlling the estimation bias of qlearningHyunKyu Jeon
This document summarizes the Maxmin Q-learning paper published at ICLR 2020. Maxmin Q-learning aims to address the overestimation bias of Q-learning and underestimation bias of Double Q-learning by maintaining multiple Q-functions and using the minimum value across them for the target in the Q-learning update. It defines the action selection and target construction for the update based on taking the maximum over the minimum Q-value for each action. The algorithm initializes multiple Q-functions, selects a random subset to update using the maxmin target constructed from the minimum Q-values. This approach reduces the biases seen in prior methods.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake