paper_id stringlengths 43 43 | summaries sequence | abstractText stringlengths 98 40k | authors list | references list | sections list | year int64 1.98k 2.02k ⌀ | title stringlengths 4 183 ⌀ |
|---|---|---|---|---|---|---|---|
SP:4d08cdb2de2044bcb574a425b42963b83fbebfbc | [
"This paper investigates kernel ridge-less regression from a stability viewpoint by deriving its risk bounds. Using stability arguments to derive risk bounds have been widely adopting in machine learning. However, related studies on kernel ridge-less regression are still sparse. The present study fills this gap, wh... | We study the average CVloo stability of kernel ridge-less regression and derive corresponding risk bounds. We show that the interpolating solution with minimum norm minimizes a bound on CVloo stability, which in turn is controlled by the condition number of the empirical kernel matrix. The latter can be characterized i... | [] | [
{
"authors": [
"Jerzy K Baksalary",
"Oskar Maria Baksalary",
"Götz Trenkler"
],
"title": "A revisitation of formulae for the moore–penrose inverse of modified matrices",
"venue": "Linear Algebra and Its Applications,",
"year": 2003
},
{
"authors": [
"Peter L. Bart... | [
{
"heading": "1 INTRODUCTION",
"text": "Statistical learning theory studies the learning properties of machine learning algorithms, and more fundamentally, the conditions under which learning from finite data is possible. In this context, classical learning theory focuses on the size of the hypothesis space... | 2,020 | null |
SP:b80bc890180934092cde037b49d94d6e4e06fad9 | [
"This paper presents a novel way of making full use of compact episodic memory to alleviate catastrophic forgetting in continual learning. This is done by adding the proposed discriminative representation loss to regularize the gradients produced by new samples. Authors gave insightful analysis on the influence of ... | The use of episodic memories in continual learning has been shown to be effective in terms of alleviating catastrophic forgetting. In recent studies, several gradientbased approaches have been developed to make more efficient use of compact episodic memories, which constrain the gradients resulting from new samples wit... | [] | [
{
"authors": [
"Rahaf Aljundi",
"Min Lin",
"Baptiste Goujaud",
"Yoshua Bengio"
],
"title": "Gradient based sample selection for online continual learning",
"venue": "In Advances in Neural Information Processing Systems,",
"year": 2019
},
{
"authors": [
"Arsl... | [
{
"heading": "1 INTRODUCTION",
"text": "In the real world, we are often faced with situations where data distributions are changing over time, and we would like to update our models by new data in time, with bounded growth in system size. These situations fall under the umbrella of “continual learning”, whi... | 2,020 | null |
SP:09f2fe6a482bbd6f9bd2c62aa841f995171ba939 | [
"This paper proposes a new framework that computes the task-specific representations to modulate the model parameters during the multi-task learning (MTL). This framework uses a single model with shared representations for learning multiple tasks together. Also, explicit task information may not be always available... | Existing Multi-Task Learning(MTL) strategies like joint or meta-learning focus more on shared learning and have little to no scope for task-specific learning. This creates the need for a distinct shared pretraining phase and a task-specific finetuning phase. The finetuning phase creates separate models for each task, w... | [] | [
{
"authors": [
"Rosana Ardila",
"Megan Branson",
"Kelly Davis",
"Michael Henretty",
"Michael Kohler",
"Josh Meyer",
"Reuben Morais",
"Lindsay Saunders",
"Francis M. Tyers",
"Gregor Weber"
],
"title": "Common voice: A massivelymultilingual speec... | [
{
"heading": "1 INTRODUCTION",
"text": "The process of Multi-Task Learning (MTL) on a set of related tasks is inspired by the patterns displayed by human learning. It involves a pretraining phase over all the tasks, followed by a finetuning phase. During pretraining, the model tries to grasp the shared know... | 2,020 | null |
SP:a1e2218e6943bf138aeb359e23628676b396ed66 | [
"This work proposes a deep reinforcement learning-based optimization strategy to the fuel optimization problem for the hybrid electric vehicle. The problem has been formulated as a fully observed stochastic Markov Decision Process (MDP). A deep neural network is used to parameterize the policy and value function. A... | This paper deals with the fuel optimization problem for hybrid electric vehicles in reinforcement learning framework. Firstly, considering the hybrid electric vehicle as a completely observable non-linear system with uncertain dynamics, we solve an open-loop deterministic optimization problem to determine a nominal opt... | [] | [
{
"authors": [
"R. Akrour",
"A. Abdolmaleki",
"H. Abdulsamad",
"G. Neumann"
],
"title": "Model Free Trajectory Optimization for Reinforcement Learning",
"venue": "In Proceedings of the International Conference on Machine Learning (ICML),",
"year": 2016
},
{
"autho... | [
{
"heading": "1 INTRODUCTION",
"text": "Hybrid electric vehicles powered by fuel cells and batteries have attracted great enthusiasm in modern days as they have the potential to eliminate emissions from the transport sector. Now, both the fuel cells and batteries have got several operational challenges whic... | 2,020 | A ROBUST FUEL OPTIMIZATION STRATEGY FOR HY- BRID ELECTRIC VEHICLES: A DEEP REINFORCEMENT LEARNING BASED CONTINUOUS TIME DESIGN AP- |
SP:43e525fb3fa611df7fd44bd3bc9843e57b154c66 | ["This paper proposes 3 deep generative models based on VAEs (with different encoding schemes for RN(...TRUNCATED) | "Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecu(...TRUNCATED) | [
{
"affiliations": [],
"name": "Zichao Yan"
},
{
"affiliations": [],
"name": "William L. Hamilton"
}
] | [{"authors":["Bronwen L Aken","Premanand Achuthan","Wasiu Akanni","M Ridwan Amode","Friederike Berns(...TRUNCATED) | [{"heading":"1 INTRODUCTION","text":"There is an increasing interest in developing deep generative m(...TRUNCATED) | 2,021 | RNA SECONDARY STRUCTURES |
SP:0bd749fe44c37b521bd40f701e1428890aaa9c95 | ["This paper presents a benchmark for discourse phenomena in machine translation. Its main novelty l(...TRUNCATED) | "Despite increasing instances of machine translation (MT) systems including extrasentential context (...TRUNCATED) | [
{
"affiliations": [],
"name": "MARKS FOR"
},
{
"affiliations": [],
"name": "DISCOURSE PHENOMENA"
}
] | [{"authors":["Rachel Bawden","Rico Sennrich","Alexandra Birch","Barry Haddow"],"title":"Evaluating d(...TRUNCATED) | [{"heading":"1 INTRODUCTION AND RELATED WORK","text":"The advances in neural machine translation (NM(...TRUNCATED) | 2,020 | DIP BENCHMARK TESTS: EVALUATION BENCH- |
SP:b2fc6ca65add04fb32bcf7622d9098de9004ca2b | ["The authors present a framework that uses a combination of VAE and GAN to recover private user i(...TRUNCATED) | "System side channels denote effects imposed on the underlying system and hardware when running a pr(...TRUNCATED) | [{"affiliations":[],"name":"Yuanyuan Yuan"},{"affiliations":[],"name":"Shuai Wang"},{"affiliations":(...TRUNCATED) | [{"authors":["Onur Aciicmez","Cetin Kaya Koc"],"title":"Trace-driven cache attacks on AES","venue":"(...TRUNCATED) | [{"heading":"1 INTRODUCTION","text":"Side channel analysis (SCA) recovers program secrets based on t(...TRUNCATED) | 2,021 | null |
SP:7fb11c941e8d79248ce5ff7caa0535a466303395 | ["This paper proposes a method of learning ensembles that adhere to an \"ensemble version\" of the i(...TRUNCATED) | "Deep ensembles perform better than a single network thanks to the diversity among their members. Re(...TRUNCATED) | [
{
"affiliations": [],
"name": "Alexandre Rame"
}
] | [{"authors":["Arturo Hernández Aguirre","Carlos A Coello Coello"],"title":"Mutual information-based(...TRUNCATED) | [{"heading":null,"text":"Deep ensembles perform better than a single network thanks to the diversity(...TRUNCATED) | 2,021 | null |
SP:5561773ab024b083be4e362db079e371abf79653 | ["The paper proposed a new training framework, namely GSL, for novel content synthesis. And GSL enab(...TRUNCATED) | "Visual cognition of primates is superior to that of artificial neural networks in its ability to (...TRUNCATED) | [{"affiliations":[],"name":"Yunhao Ge"},{"affiliations":[],"name":"Sami Abu-El-Haija"},{"affiliation(...TRUNCATED) | [{"authors":["Yuval Atzmon","Gal Chechik"],"title":"Probabilistic and-or attribute grouping for zero(...TRUNCATED) | [{"heading":"1 INTRODUCTION","text":"Primates perform well at generalization tasks. If presented wit(...TRUNCATED) | 2,021 | ZERO-SHOT SYNTHESIS WITH GROUP-SUPERVISED LEARNING |
SP:9f70871f0111b58783f731748d8750c635998f32 | ["This paper presents an approach to learn goal conditioned policies by relying on self-play which s(...TRUNCATED) | "We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, includin(...TRUNCATED) | [] | [{"authors":["Marcin Andrychowicz","Filip Wolski","Alex Ray","Jonas Schneider","Rachel Fong","Peter (...TRUNCATED) | [{"heading":"1 INTRODUCTION","text":"We are motivated to train a single goal-conditioned policy (Kae(...TRUNCATED) | 2,020 | null |
End of preview. Expand in Data Studio
MuP - Multi Perspective Scientific Document Summarization
Generating summaries of scientific documents is known to be a challenging task. Majority of existing work in summarization assumes only one single best gold summary for each given document. Having only one gold summary negatively impacts our ability to evaluate the quality of summarization systems as writing summaries is a subjective activity. At the same time, annotating multiple gold summaries for scientific documents can be extremely expensive as it requires domain experts to read and understand long scientific documents. This shared task will enable exploring methods for generating multi-perspective summaries. We introduce a novel summarization corpus, leveraging data from scientific peer reviews to capture diverse perspectives from the reader's point of view.
For more information about the dataset please refer to: https://github.com/allenai/mup
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