39 research outputs found
Deep Clustering Survival Machines with Interpretable Expert Distributions
Conventional survival analysis methods are typically ineffective to
characterize heterogeneity in the population while such information can be used
to assist predictive modeling. In this study, we propose a hybrid survival
analysis method, referred to as deep clustering survival machines, that
combines the discriminative and generative mechanisms. Similar to the mixture
models, we assume that the timing information of survival data is generatively
described by a mixture of certain numbers of parametric distributions, i.e.,
expert distributions. We learn weights of the expert distributions for
individual instances according to their features discriminatively such that
each instance's survival information can be characterized by a weighted
combination of the learned constant expert distributions. This method also
facilitates interpretable subgrouping/clustering of all instances according to
their associated expert distributions. Extensive experiments on both real and
synthetic datasets have demonstrated that the method is capable of obtaining
promising clustering results and competitive time-to-event predicting
performance
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
As deep learning models nowadays are widely adopted by both cloud services
and edge devices, reducing the latency of deep learning model inferences
becomes crucial to provide efficient model serving. However, it is challenging
to develop efficient tensor programs for deep learning operators due to the
high complexity of modern accelerators and the rapidly growing number of
operators. Deep learning compilers, such as Apache TVM, adopt declarative
scheduling primitives to lower the bar of developing tensor programs. However,
we show that this approach is insufficient to cover state-of-the-art tensor
program optimizations. In this paper, we propose to embed the scheduling
process into tensor programs and use dedicated mappings, called task mappings,
to define the computation assignment and ordering. This new approach greatly
enriches the expressible optimizations by allowing developers to manipulate
tensor programs at a much finer granularity. We call the proposed method the
task-mapping programming paradigm. In addition, we propose a new
post-scheduling fusion optimization that allows developers to focus on
scheduling every single operator and automates the fusion after scheduling. It
greatly reduces the engineering efforts for operator fusion. Our proposed
paradigm also constructs an efficient hardware-centric schedule space, which is
agnostic to the program input size and greatly reduces the tuning time. With
the proposed paradigm, we implement a deep learning compiler Hidet. Extensive
experiments on modern convolution and transformer models show that Hidet
outperforms state-of-the-art DNN inference framework, ONNX Runtime, and
compiler, TVM equipped with scheduler AutoTVM and Ansor, by up to 1.48x (1.22x
on average). It also reduces the tuning time by 20x and 11x compared with
AutoTVM and Ansor, respectively. We open-sourced hidet at
https://www.github.com/hidet-org/hidet.Comment: 15 pages, 22 figures, 1 tabl
SARS-CoV Infection in a Restaurant from Palm Civet
Contact with food animals was associated with SARS-CoV infection in the People’s Republic of China
A prospective study of rotavirus infections
published_or_final_versionMicrobiologyDoctoralDoctor of Philosoph
A multi-label text classification model based on ELMo and attention
Text classification is a common application in natural language processing. We proposed a multi-label text classification model based on ELMo and attention mechanism which help solve the problem for the sentiment classification task that there is no grammar or writing convention in power supply related text and the sentiment related information disperses in the text. Firstly, we use pre-trained word embedding vector to extract the feature of text from the Internet. Secondly, the analyzed deep information features are weighted according to the attention mechanism. Finally, an improved ELMo model in which we replace the LSTM module with GRU module is used to characterize the text and information is classified. The experimental results on Kaggle’s toxic comment classification data set show that the accuracy of sentiment classification is as high as 98%
A multi-label text classification model based on ELMo and attention
Text classification is a common application in natural language processing. We proposed a multi-label text classification model based on ELMo and attention mechanism which help solve the problem for the sentiment classification task that there is no grammar or writing convention in power supply related text and the sentiment related information disperses in the text. Firstly, we use pre-trained word embedding vector to extract the feature of text from the Internet. Secondly, the analyzed deep information features are weighted according to the attention mechanism. Finally, an improved ELMo model in which we replace the LSTM module with GRU module is used to characterize the text and information is classified. The experimental results on Kaggle’s toxic comment classification data set show that the accuracy of sentiment classification is as high as 98%
Research and implementation of intelligent decision based on a priori knowledge and DQN algorithms in wargame environment
The reinforcement learning problem of complex action control in a multi-player wargame has been a hot research topic in recent years. In this paper, a game system based on turn-based confrontation is designed and implemented with state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based on the DQN (Deep Q Network) to model complex game behaviors. Then, an a priori knowledge-based algorithm PK-DQN (Prior Knowledge-Deep Q Network) is introduced to improve the DQN algorithm, which accelerates the convergence speed and stability of the algorithm. The experiments demonstrate the correctness of the PK-DQN algorithm, it is validated, and its performance surpasses the conventional DQN algorithm. Furthermore, the PK-DQN algorithm shows effectiveness in defeating the high level of rule-based opponents, which provides promising results for the exploration of the field of smart chess and intelligent game deductionInnovation and Creativity Research Program for Doctoral Students of Nanjing University (grant number CXCY19-19
Research on Action Strategies and Simulations of DRL and MCTS-based Intelligent Round Game
The reinforcement learning problem of complex action control in multiplayer online battlefield games has brought considerable interest in the deep learning field. This problem involves more complex states and action spaces than traditional confrontation games, making it difficult to search for any strategy with human-level performance. This paper presents a deep reinforcement learning model to solve this problem from the perspective of game simulations and algorithm implementation. A reverse reinforcement-learning model based on high-level player training data is established to support downstream algorithms. With less training data, the proposed model is converged quicker, and more consistent with the action strategies of high-level players’ decision-making. Then an intelligent deduction algorithm based on DDQN is developed to achieve a better generalization ability under the guidance of a given reward function. At the game simulation level, this paper constructs Monte Carlo Tree Search Intelligent Decision Model for turn-based antagonistic deduction games to generate next-step actions. Furthermore, a prototype game simulator that combines offline with online functions is implemented to verify the performance of proposed model and algorithm. The experiments show that our proposed approach not only has a better reference value to the antagonistic environment using incomplete information, but also accurate and effective in predicting the return value. Moreover, our work provides a theoretical validation platform and testbed for related research on game AI for deductive games.N/