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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:
In this paper, we propose a novel kind of kernel, random forest kernel, to enhance the empirical performanc... | A self-attention network for RNN/CNN-free sequence encoding with small memory consumption, highly parallelizable computation and state-of-the-art performance on several NLP tasks
| 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.
Human reasoning involves recognising common underlying principles across many examples by utilising variables. The by... | Learning to Search Efficient DenseNet with Layer-wise Pruning
| 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.
Q: In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing d... | We propose a quantization scheme for weights and activations of deep neural networks. This reduces the memory footprint substantially and accelerates inference.
****
| 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.
Example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: We introduce unsupervised continual learning (UCL) and a neuro-inspired architecture that solves the UCL problem. | 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.
Let me give you an example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and... | We present scene programs, a structured scene representation that captures both low-level object appearance and high-level regularity in the scene. | 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: Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generati... | Output: Gen-RKM: a novel framework for generative models using Restricted Kernel Machines with multi-view generation and uncorrelated feature learning.
| 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... | Allowing partial channel connection in super-networks to regularize and accelerate differentiable architecture search | 9 | 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 you should evaluate adversarial attacks on seq2seq | 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: Optimization algorithms for training deep models not only affects the convergence rate and stability of the traini... | Allowing partial channel connection in super-networks to regularize and accelerate differentiable architecture search
****
| 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.
Example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: Simple and effective graph neural network with mixture of random walk steps and attention | 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: In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there ... | Output: How you should evaluate adversarial attacks on seq2seq
| 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.
Ex Input:
Recurrent neural network(RNN) is an effective neural network in solving very complex supervised and unsuper... | Disentanglement-PyTorch is a library for variational representation learning
| 1 | 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... | Inference of a mean field game (MFG) model of large population behavior via a synthesis of MFG and Markov decision processes. | 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... | We learn a representation of an agent's action space from pure visual observations. We use a recurrent latent variable approach with a novel composability loss. | 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... | Simulation to real images translation and video generation | 8 | 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... | We propose a new framework for preconditioner learning, derive new forms of preconditioners and learning methods, and reveal the relationship to methods like RMSProp, Adam, Adagrad, ESGD, KFAC, batch normalization, etc. | 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... | We make convolutional layers run faster by dynamically boosting and suppressing channels in feature computation. | 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... | A sober view on the current state of GANs from a practical perspective | 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 compare robustness of models from 4 popular NLP tasks: Q&A, NLI, NER and Sentiment Analysis by testing their performance on perturbed inputs. | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: Propose an assessment framework to analyze and learn graph convolutional filter | 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... | Proposed a method to extract and leverage interpretations of feature interactions | 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 deep latent variable MRFs with a saddle-point objective derived from the Bethe partition function approximation. | 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.
--------
Question: Modern neural network architectures use structured linear transformations, such as low-rank matrice... | routing networks: a new kind of neural network which learns to adaptively route its input for multi-task learning
| 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: A typical experiment to study cognitive function is to train animals to perform tasks, while the resea... | Proposed a method to extract and leverage interpretations of feature interactions
| 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... | A novel adversarial detection approach, which uses explainability methods to identify images whose explanations are inconsistent with the predicted class. | 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.
Input: Consider Input: Goal recognition is the problem of inferring the correct goal towards which an agent executes ... | Output: Accelerating SGD by arranging examples differently
| 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... | We show that question-answer matching is a particularly good pre-training task for question-similarity and release a dataset for medical question similarity | 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.
Let me give you an example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and... | In network pruning, fine-tuning a pruned model only gives comparable or worse performance than training it from scratch. This advocate a rethinking of existing pruning algorithms. | 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.
--------
Question: As the basic building block of Convolutional Neural Networks (CNNs), the convolutional layer is des... | Policy optimization by using past good rollouts from the agent; learning shaped rewards via divergence minimization; SVPG with JS-kernel for population-based exploration.
| 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]: While extremely successful in several applications, especially with low-level representations; sparse, noisy ... | Dynamic model that learns divide and conquer strategies by weak supervision.
| 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 investigate the space efficiency of memory-augmented neural nets when learning set membership. | 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 Input:
Protein classification is responsible for the biological sequence, we came up with an idea whichdeals with ... | we present the state-of-the-art results of using neural networks to diagnose chest x-rays
| 1 | 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:
The softmax function is widely used to train deep neural networks for multi-class classificatio... | We investigate the space efficiency of memory-augmented neural nets when learning set membership.
| 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 introduce a parameter sharing scheme, in which different layers of a convolutional neural... | We propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition
| 8 | 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... | Anticipation improves convergence of deep reinforcement learning. | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficien... | Lower bound for compressed sensing w/ generative models that matches known upper bounds | 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... | We provide a new perspective on training a machine learning model from scratch in hierarchical label setting, i.e. thinking of it as two-way communication between human and algorithms, and study how we can both measure and improve the efficiency. | 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... | Compression of Deep neural networks deployed on embedded device. | 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:
State of the art computer vision models have been shown to be vulnerable to small adversarial perturbat... | This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes
| 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... | This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes | 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: When considering simultaneously a finite number of tasks, multi-output learning enables one to account for the sim... | Proposed RNN-based algorithm to estimate predictive distribution in one- and multi-step forecasts in time series prediction problems
****
| 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.
--------
Question: We propose Automating Science Journalism (ASJ), the process of producing a press release from a sci... | We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.
| 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... | Exploiting rich strucural details in graph-structued data via adaptive "strucutral fingerprints'' | 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 example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | In visual prediction tasks, letting your predictive model choose which times to predict does two things: (i) improves prediction quality, and (ii) leads to semantically coherent "bottleneck state" predictions, which are useful for planning. | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | This paper presents a GAN-based framework for learning the distribution from high-dimensional incomplete data. | 3 | 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 introduce Variational Intrinsic Successor FeatuRes (VISR), a novel algorithm which learns controllable features that can be leveraged to provide fast task inference through the successor features framework. | 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: Variational inference (VI) is a popular approach for approximate Bayesian inference that is particular... | Exploiting rich strucural details in graph-structued data via adaptive "strucutral fingerprints''
| 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: theoretically understand the regularization effect of distillation. We show that early stopping is essential in this process. From this perspective, we developed a distillation method for learning with corrupted Label with theoretical guarantees. | 5 | 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: In the field of Generative Adversarial Networks (GANs), how to design a stable training stra... | In visual prediction tasks, letting your predictive model choose which times to predict does two things: (i) improves prediction quality, and (ii) leads to semantically coherent "bottleneck state" predictions, which are useful for planning.
| 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]: Understanding how people represent categories is a core problem in cognitive science, with the flexibility of hu... | We propose a generative latent variable model for unsupervised scene decomposition that provides factorized object representation per foreground object while also decomposing background segments of complex morphology.
| 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.
Let me give you an example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and... | Redistributing and growing weights according to the momentum magnitude enables the training of sparse networks from random initializations that can reach dense performance levels with 5% to 50% weights while accelerating training by up to 5.6x. | 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... | We propose a new approach to train GANs with a mixture of generators to overcome the mode collapsing problem. | 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: In this paper we use the geometric properties of the optimal transport (OT) problem and the ... | A deep RL algorithm for solving POMDPs by auto-encoding the underlying states using a variational recurrent 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.
One example is below.
Q: Mixed precision training (MPT) is becoming a practical technique to improve the speed and ene... | a new framework using dual space for generating images corresponding to multiclass labels when the number of class is large | 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: We consider reinforcement learning and bandit structured prediction problems with very sparse ... | Output: We address sample inefficiency and reward bias in adversarial imitation learning algorithms such as GAIL and AIRL.
| 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]: Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as Imag... | Generates never seen data during training from a desired condition
| 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... | We introduce DPFRL, a framework for reinforcement learning under partial and complex observations with a fully differentiable discriminative particle filter | 6 | 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... | Learning to synthesize raw waveform audio with GANs | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: We propose the use of optimistic mirror decent to address cycling problems in the training of GANs. We also introduce the Optimistic Adam algorithm | 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 flow-based autoregressive model for molecular graph generation. Reaching state-of-the-art results on molecule generation and properties optimization. | 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 present a simple neural model that given a formula and a property tries to answer the questi... | We propose an efficient recurrent network model for forward prediction on time-varying distributions.
| 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.
--------
Question: Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, e... | Learning to synthesize raw waveform audio with GANs
| 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... | Combining orthogonal model compression techniques to get significant reduction in model size and number of flops required during inferencing. | 4 | 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 analyze recurrent networks trained on sentiment classification, and find that they all exhibit approximate line attractor dynamics when solving this task. | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficien... | Conventional memory networks generate many redundant latent vectors resulting in overfitting and the need for larger memories. We introduce memory dropout as an automatic technique that encourages diversity in the latent space. | 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.
Input: Consider Input: We present a graph neural network assisted Monte Carlo Tree Search approach for the classical ... | Output: We propose four new ways of collecting NLI data. Some help slightly as pretraining data, all help reduce annotation artifacts.
| 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... | We propose CR-NAS to reallocate engaged computation resources in different resolution and spatial position. | 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 Input:
We investigate methods for semi-supervised learning (SSL) of a neural linear-chain conditional random field... | We combine Multi-output Gaussian processes with deep recurrent Q-networks to learn optimal treatments for sepsis and show improved performance over standard deep reinforcement learning methods,
| 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... | We present a novel black-box targeted attack that is able to fool state of the art speech to text transcription. | 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... | We show that individual units in CNN representations learned in NLP tasks are selectively responsive to specific natural language concepts. | 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... | We present a variational lower bound for GP models that can be optimised without computing expensive matrix operations like inverses, while providing the same guarantees as existing variational approximations. | 6 | 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: Empirical risk minimization (ERM), with proper loss function and regularization, is the comm... | Conventional memory networks generate many redundant latent vectors resulting in overfitting and the need for larger memories. We introduce memory dropout as an automatic technique that encourages diversity in the latent space.
| 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: We show that posterior collapse in linear VAEs is caused entirely by marginal log-likelihood (not ELBO). Experiments on deep VAEs suggest a similar phenomenon is at play. | 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.
[Q]: Recent work on encoder-decoder models for sequence-to-sequence mapping has shown that integrating both temporal ... | It can generate effective hash codes for efficient cold-start recommendation and meanwhile provide a feasible marketing strategy.
| 5 | 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 good tagger gives similar tags to a given paper and the papers it cites | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency o... | Solution: We revisit the idea of the master-slave architecture in multi-agent deep reinforcement learning and outperforms state-of-the-arts. | 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 Input:
Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensio... | Our hypothesis is that given two domains, the lowest complexity mapping that has a low discrepancy approximates the target mapping.
| 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: We present Line-Storm, an interactive computer system for creative performance. The context we inve... | We present a low-bias estimator for Boolean stochastic variable models with many stochastic layers.
| 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.
For typical sequence prediction problems such as language generation, maximum likelihood estimation (MLE) has commonl... | A framework that conducts online refinement of pseudo labels with a novel soft softmax-triplet loss for unsupervised domain adaptation on person re-identification.
| 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... | We develop VAEs where the encoder takes a model parameter vector as input, so we can do rapid inference for many models | 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... | A novel architecture for few-shot classification capable of dealing with uncertainty. | 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 Q]: A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that ... | We prove that gradient descent is robust to label corruption despite over-parameterization under a rich dataset model.
| 6 | 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... | We analyze the training process for Deep Networks and show that they start from rapidly learning shallow classifiable examples and slowly generalize to harder data points. | 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... | We propose a neural network that is able to generate topic-specific questions. | 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.
Input: Consider Input: Knowledge Bases (KBs) are becoming increasingly large, sparse and probabilistic. These KBs are... | Output: We introduce and analyze several criteria for detecting overfitting.
| 2 | 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... | Using deep learning techniques on singing voice related 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.
Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could co... | Efficiently inducing low-rank deep neural networks via SVD training with sparse singular values and orthogonal singular vectors.
| 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 input: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | Label-efficient audio classification via multi-task learning and self-supervision | 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... | This paper describes and analyzes three methods to schedule non-fixed duration activities in the presence of consumptive resources. | 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: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | Prune and ReLU in Winograd domain for efficient convolutional neural network | 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: Transfer learning through fine-tuning a pre-trained neural network with an extremely large d... | An extension of GANs combining optimal transport in primal form with an energy distance defined in an adversarially learned feature space.
| 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 Q]: Recurrent neural network(RNN) is an effective neural network in solving very complex supervised and unsupervi... | A technique for accelerating neural architecture selection by approximating the weights of each candidate architecture instead of training them individually.
| 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... | A method to transform DNA sequences into 2D images using space-filling Hilbert Curves to enhance the strengths of CNNs | 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... | This work enforces Hamiltonian dynamics with control to learn system models from embedded position and velocity data, and exploits this physically-consistent dynamics to synthesize model-based control via energy shaping. | 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.
Q: Deep networks run with low precision operations at inference time offer power and space advantages over high preci... | Refining segmentation proposals by performing iterative inference with conditional denoising autoencoders.
****
| 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.
Example input: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy effic... | We present a new framework for adapting Adam-typed methods, namely AdamT, to include the trend information when updating the parameters with the adaptive step size and gradients. | 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.
[Q]: This paper presents preliminary ideas of our work for auto- mated learning of Hierarchical Goal Networks in nond... | In this paper we propose a novel generative model to craft systematic poisoning attacks with detectability constraints against machine learning classifiers, including deep networks.
| 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... | Reinforcement learning and Adaptive Sampling for Optimized Compilation of Deep Neural Networks. | 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]: We introduce geomstats, a Python package for Riemannian modelization and optimization over manifolds such as ... | We propose an attention-invariant attack method to generate more transferable adversarial examples for black-box attacks, which can fool state-of-the-art defenses with a high success rate.
| 6 | 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... | It is shown that ResNet-type CNNs are a universal approximator and its expression ability is not worse than fully connected neural networks (FNNs) with a \textit{block-sparse} structure even if the size of each layer in the CNN is fixed. | 2 | NIv2 | task668_extreme_abstract_summarization | fs_opt |
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