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Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
See one example below:
Problem: Mixed precision training (MPT) is becoming a practical techniqu... | Improved training of current flow-based generative models (Glow and RealNVP) on density estimation benchmarks | 4 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
[Q]: We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward syn... | Pros and cons of saccade-based computer vision under a predictive coding perspective
| 5 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Consider Input: We propose an approach for sequence modeling based on autoregressive normalizing flows. Each a... | Output: We introduce a novel method to train Seq2Seq models with language models that converge faster, generalize better and can almost completely transfer to a new domain using less than 10% of labeled data.
| 2 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | We enhance existing transformation-based defenses by using a distribution classifier on the distribution of softmax obtained from transformed images. | 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Let me give you an example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and... | We consider a simplified deep convolutional neural network model. We show that all layers of this network can be approximately learned with a proper application of tensor decomposition. | 8 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: Consider Input: In the field of Generative Adversarial Networks (GANs), how to design a stable training strate... | Output: Interactive technique to improve brushing in dense trajectory datasets by taking into account the shape of the brush.
| 2 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Part 1. Definition
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Part 2. Example
Mixed precision training (MPT) is becoming a practical technique to improve the spe... | A new loss based on relatively hard negatives that achieves state-of-the-art performance in image-caption retrieval. | 7 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Given the task definition, example input & output, solve the new input case.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example: Mixed precision training (MPT) i... | We propose to train an Invertible Neural Network for each class to perform class-by-class Continual Learning. | 1 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Part 1. Definition
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Part 2. Example
Mixed precision training (MPT) is becoming a practical technique to improve the spe... | We learn deep representation by maximizing mutual information, leveraging structure in the objective, and are able to compute with fully supervised classifiers with comparable architectures | 7 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example Input: Developing agents that can learn to follow natural language instructions has been an emerging research... | We propose a novel attention networks with the hybird encoder to solve the text representation issue of Chinese text classification, especially the language phenomena about pronunciations such as the polyphone and the homophone.
| 3 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example input: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | We propose a novel attention networks with the hybird encoder to solve the text representation issue of Chinese text classification, especially the language phenomena about pronunciations such as the polyphone and the homophone. | 3 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Ex Input:
System identification is the process of building a mathematical model of an unknown system from measurement... | We augment the Q-value estimates with a count-based bonus that ensures optimism during action selection and bootstrapping, even if the Q-value estimates are pessimistic.
| 1 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
See one example below:
Problem: Mixed precision training (MPT) is becoming a practical techniqu... | A set of methods to obtain uncertainty estimation of any given model without re-designing, re-training, or to fine-tuning it. | 4 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
[EX Q]: Neural networks exhibit good generalization behavior in the
over-parameterized regime, where the number of n... | a joint model and gradient sparsification method for federated learning
| 6 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
Style transfer usually refers to the task of applying color and texture information from a spec... | DISENTANGLED STATE SPACE MODELS
| 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example Input: We characterize the singular values of the linear transformation associated with a standard 2D multi-c... | We present a method to learn interpretable representations on time series using ideas from variational autoencoders, self-organizing maps and probabilistic models.
| 3 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: Deep networks often perform well on the data distribution on which they are trained, yet giv... | We propose a novel method named Maximal Divergence Sequential Auto-Encoder that leverages Variational AutoEncoder representation for binary code vulnerability detection.
| 8 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 word... | Fine-tuning BERT on legal corpora provides marginal, but valuable, improvements on NLP tasks in the legal domain. | 0 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Let me give you an example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and... | GLMP: Global memory encoder (context RNN, global pointer) and local memory decoder (sketch RNN, local pointer) that share external knowledge (MemNN) are proposed to strengthen response generation in task-oriented dialogue. | 8 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: Generative adversarial networks (GANs) are a family of generative models that do not minimiz... | harmonic acoustic model
| 8 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Let me give you an example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and... | We propose supervising VAE-style topic models by intelligently adjusting the prior on a per document basis. We find a logit-normal posterior provides the best performance. | 8 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Part 1. Definition
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Part 2. Example
Mixed precision training (MPT) is becoming a practical technique to improve the spe... | This paper presents a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. | 7 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
See one example below:
Problem: Mixed precision training (MPT) is becoming a practical techniqu... | We use sparsity to improve the computational complexity of variance reduction methods. | 4 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | Embedded architecture for deep learning on optimized devices for face detection and emotion recognition | 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: We propose a rejection sampling scheme using the discriminator of a GAN to
approximately co... | Metareasoning in a Situated Temporal Planner
| 8 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Ex Input:
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding... | This work proposed a universal visual representation for neural machine translation (NMT) using retrieved images with similar topics to source sentence, extending image applicability in NMT.
| 1 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example input: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | We propose a self-ensemble framework to train more robust deep learning models under noisy labeled datasets. | 3 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | We propose an approach that endows a single model with the ability to represent both extremes: joint training and independent training, which leads to effective multi-task learning. | 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | We show how you can boost performance in a multitask network by tuning an adaptive multitask loss function that is learned through directly balancing network gradients. | 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: We propose several intrinsic reward functions for encouraging coordinated exploration in multi-agent problems, and introduce an approach to dynamically selecting the best exploration method for a given task, online. | 5 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | This work proposed a universal visual representation for neural machine translation (NMT) using retrieved images with similar topics to source sentence, extending image applicability in NMT. | 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
[EX Q]: Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unim... | We propose a method to deal with rare words by computing their embedding from definitions.
| 6 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 word... | Learning compositional Koopman operators for efficient system identification and model-based control. | 0 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
TASK DEFINITION: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
PROBLEM: Ordinary stochastic neural networks mostly rely on the expected values of their weights to m... | Learning compositional Koopman operators for efficient system identification and model-based control.
| 8 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example input: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | Scalable and low communication load balancing solution for heterogeneous-server multi-dispatcher systems with strong theoretical guarantees and promising empirical results. | 3 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
--------
Question: We show that information about whether a neural network's output will be correct or incorrect is pr... | A quantum inspired kernel for convolution network, exhibiting interference phenomena, can be very useful (and compared it with real value counterpart).
| 7 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
[EX Q]: Trading off exploration and exploitation in an unknown environment is key to maximising expected return durin... | We use simple and biologically motivated modifications of standard learning techniques to achieve state of the art performance on catastrophic forgetting benchmarks.
| 6 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | We take a step towards measuring learning task difficulty and demonstrate that in practice performance strongly depends on the match of the representation of the information and the model interpreting it. | 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | We address the active learning in batch setting with noisy oracles and use model uncertainty to encode the decision quality of active learning algorithm during acquisition. | 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example Input: This paper develops variational continual learning (VCL), a simple but general framework for continual... | Train GANs with differential privacy to generate artificial privacy-preserving datasets.
| 3 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
See one example below:
Problem: Mixed precision training (MPT) is becoming a practical techniqu... | Graph regularization forces spectral embedding to focus on the largest clusters, making the representation less sensitive to noise. | 4 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Ex Input:
Style transfer usually refers to the task of applying color and texture information from a specific style i... | A stable domain-adversarial training approach for robust and comprehensive domain adaptation
| 1 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Let me give you an example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and... | A weakly supervised learning based clustering framework performs comparable to that of fully supervised learning models by exploiting unique class count. | 8 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
[Q]: This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker. Autostacker... | Representing the network architecture as a set of syntax trees and optimizing their structure leads to accurate and concise regression models.
| 5 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example input: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | A stable domain-adversarial training approach for robust and comprehensive domain adaptation | 3 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficien... | This paper studies the interactions between the fast-learning and slow-prediction models and demonstrate how such interactions can improve machine capability to solve the joint lifelong and few-shot learning problems. | 6 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Ex Input:
The Lottery Ticket Hypothesis from Frankle & Carbin (2019) conjectures that, for typically-sized neural net... | A weakly supervised learning based clustering framework performs comparable to that of fully supervised learning models by exploiting unique class count.
| 1 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
--------
Question: Graph neural networks have recently achieved great successes in predicting quantum mechanical prope... | We introduce a data-driven Distributed Source Coding framework based on Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC).
| 7 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Given the task definition, example input & output, solve the new input case.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example: Mixed precision training (MPT) i... | A fast optimizer for general applications and large-batch training. | 1 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One of the most successful techniques in generative models has been decomposing a complicated generation task into a ... | See the abstract. (For the revision, the paper is identical, except for a 59 page Supplementary Material, which can serve as a stand-along technical report version of the paper.)
| 0 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Teacher: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Mixed precision t... | A quantitative refinement of the universal approximation theorem via an algebraic approach. | 2 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
One example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficien... | A new knowledge distill method for transfer learning | 6 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
[EX Q]: It is clear that users should own and control their data and privacy. Utility providers are also becoming mor... | A fast optimizer for general applications and large-batch training.
| 6 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
Given the task definition, example input & output, solve the new input case.
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Example: Mixed precision training (MPT) i... | Make the transformer streamable with monotonic attention. | 1 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
[Q]: We develop a reinforcement learning based search assistant which can assist users through a set of actions and s... | We propose a novel architecture that traverses an image pyramid in a top-down fashion, while it visits only the most informative regions along the way.
| 5 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
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