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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 |
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... | In this paper, we develop fast retraining-free sparsification methods that can be deployed for on-the-fly sparsification of CNNs in many industrial contexts. | 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 a novel framework to evaluate the interpretability of neural network. | 9 | 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 propose a differentiable product quantization framework that can reduce the size of embedding layer in an end-to-end training at no performance cost. | 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.
Building on the success of deep learning, two modern approaches to learn a probability model of the observed data are... | We propose a novel framework to evaluate the interpretability of neural network.
| 0 | 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... | Efficient video classification using frame-based conditional gating module for selecting most-dominant frames, followed by temporal modeling and classifier. | 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]: Dramatic advances in generative models have resulted in near photographic quality for artificially rendered face... | Efficient video classification using frame-based conditional gating module for selecting most-dominant frames, followed by temporal modeling and classifier.
| 5 | 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 simple architecture consisting of convolutions and attention achieves results on par with the best documented recurrent models. | 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.
Example Input: Despite the fact that generative models are extremely successful in practice, the theory underlying th... | An approximate inference algorithm for deep learning
| 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: Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative mo... | We show how to make predictions using deep networks, without training deep networks.
| 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.
--------
Question: High performance of deep learning models typically comes at cost of considerable model size and com... | Accelerate distributed optimization by exploiting stragglers.
| 7 | 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:
Plagiarism and text reuse become more available with the Internet development. Therefore it is ... | Presents new architecture which leverages information globalization power of u-nets in a deeper networks and performs well across tasks without any bells and whistles.
| 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.
[Q]: Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, g... | We introduce the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended to policy gradient methods.
| 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 develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditi... | Output: We train an image to image translation network that take as input the source image and a sample from a prior distribution to generate a sample from the target distribution
| 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.
Q: We consider the problem of generating plausible and diverse video sequences, when we are only given a start and an... | New method for assessing the quaility of similarity evaluators and showing potential of Transformer-based language models in replacing BLEU and ROUGE.
****
| 4 | 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... | New method for assessing the quaility of similarity evaluators and showing potential of Transformer-based language models in replacing BLEU and ROUGE. | 4 | 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: Trading off exploration and exploitation in an unknown environment is key to maximising expe... | We provide a method to benchmark optimizers that is cognizant to the hyperparameter tuning process.
| 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... | We propose temporal self-supervisions for learning stable temporal functions with GANs. | 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.
--------
Question: The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to hum... | A practical and provably guaranteed approach for training efficiently classifiers in the presence of label shifts between Source and Target data sets
| 7 | 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... | a novel method to learn with sparse reward using adversarial reward re-labeling | 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.
One example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficien... | We introduce an effective, general framework for incorporating conditioning information into inference-based generative models. | 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.
Example input: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | We proposed two new approaches, the incremental sliced inverse regression and incremental overlapping sliced inverse regression, to implement supervised dimension reduction in an online learning manner. | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: A method to model the generative distribution of sequences coming from graph connected entities. | 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: As Artificial Intelligence (AI) becomes an integral part of our life, the development of explainable A... | We present a RL agent MINERVA which learns to walk on a knowledge graph and answer queries
| 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.
Recurrent neural networks (RNNs) can model natural language by sequentially ''reading'' input tokens and outputting a... | We derive a new PAC-Bayesian Bound for unbounded loss functions (e.g. Negative Log-Likelihood).
| 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.
[EX Q]: We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al f... | The paper uses Variational Auto-Encoding and network conditioning for Musical Timbre Transfer, we develop and generalize our architecture for many-to-many instrument transfers together with visualizations and evaluations.
| 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.
Example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: We propose a GAN variant which learns to generate point clouds. Different studies have been explores, including tighter Wasserstein distance estimate, conditional generation, generalization to unseen point clouds and image to point cloud. | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficien... | The paper uses Variational Auto-Encoding and network conditioning for Musical Timbre Transfer, we develop and generalize our architecture for many-to-many instrument transfers together with visualizations and evaluations. | 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]: We present local ensembles, a method for detecting extrapolation at test time in a pre-trained model. We focu... | We propose a mechanism for denoising the internal state of an RNN to improve generalization performance.
| 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.
Despite the fact that generative models are extremely successful in practice, the theory underlying this phenomenon i... | We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound.
| 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.
Example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound. | 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: It is clear that users should own and control their data and privacy. Utility providers are also becom... | How to Training 100,000 classes on a single GPU
| 3 | 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... | How to Training 100,000 classes on a single GPU | 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.
[Q]: In this paper, we propose a \textit{weak supervision} framework for neural ranking tasks based on the data progr... | Combining classification and image retrieval in a neural network architecture, we obtain an improvement for both tasks.
| 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.
[EX Q]: Recurrent Neural Networks architectures excel at processing sequences by
modelling dependencies over differe... | We propose DVD-GAN, a large video generative model that is state of the art on several tasks and produces highly complex videos when trained on large real world datasets.
| 6 | 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 propose DVD-GAN, a large video generative model that is state of the art on several tasks and produces highly complex videos when trained on large real world datasets. | 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.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | We develop engaging image captioning models conditioned on personality that are also state of the art on regular captioning tasks. | 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.
Input: Consider Input: The Convolutional Neural Network (CNN) has been successfully applied in many fields during rec... | Output: We propose a new method for enhancing the transferability of adversarial examples by using the noise-reduced gradient.
| 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 augment model-free policy learning with a sequence-level surrogate reward functions and count-based visitation bonus and demonstrate effectiveness in the large batch, low-round regime seen in designing DNA and protein sequences. | 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 propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and bandwidth-efficient way to utilize existing neural personalization techniques in the Federated Learning (FL) setting. | 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.
Input: Consider Input: Claims from the fields of network neuroscience and connectomics suggest that topological model... | Output: We develop engaging image captioning models conditioned on personality that are also state of the art on regular captioning tasks.
| 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.
[Q]: We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic ... | A general framework for incorporating long-term safety constraints in policy-based reinforcement learning
| 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.
Protein classification is responsible for the biological sequence, we came up with an idea whichdeals with the classi... | In this paper, we propose a three-dimensional regularization-based pruning method to accelerate the 3D-CNN.
| 0 | 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 new approach for learning with noisy rewards in reinforcement learning | 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.
Input: Consider Input: In this work we present a new agent architecture, called Reactor, which combines multiple algo... | Output: A new approach for learning with noisy rewards in reinforcement learning
| 2 | 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:
We propose a novel deep network architecture for lifelong learning which we refer to as Dynamic... | We perform counting for visual question answering; our model produces interpretable outputs by counting directly from detected objects.
| 9 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
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