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5月26日晚7:30-9:00

AI TIME特别邀请了三位优秀的讲者跟大家共同开启ICLR专场二！

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链接：https://live.bilibili.com/21813994

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秦增益：MIT一年级博士生，本科毕业于清华大学电子工程系，以第一作者在ICML, ICLR, CVPR, AAAI, TPAMI, ACM MM, ICRA等知名会议和期刊发表多篇论文。曾获清华大学挑战杯特等奖，北京大学生挑战杯特等奖。

报告题目：

多智能体的安全控制

摘要：

We study the multi-agent safe control problem where agents should avoid collisions to static obstacles and collisions with each other while reaching their goals. Our core idea is to learn the multi-agent control policy jointly with learning the control barrier functions as safety certificates. We propose a novel joint-learning framework that can be implemented in a decentralized fashion, which can adapt to an arbitrarily large number of agents. Our approach also shows exceptional generalization capability in that the control policy can be trained with 8 agents in one scenario, while being used on other scenarios with up to 1024 agents in complex multi-agent environments.

论文标题：

Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates

论文链接：

https://arxiv.org/abs/2101.05436

袁渊源：本科毕业于复旦大学，现为香港科技大学计算机科学与工程系一年级博士生，导师为王帅教授。

报告题目：

基于生成模型的系统侧信道攻击

摘要：

System side channels denote effects imposed on the underlying system and hardware when running a program, such as its accessed CPU cache lines. Side channel analysis (SCA) allows attackers to infer program secrets based on observed side channel logs. Given the ever-growing adoption of machine learning as a service (MLaaS), image analysis software on cloud platforms has been exploited by reconstructing private user images from system side channels. Nevertheless, to date, SCA is still highly challenging, requiring technical knowledge of victim software’s internal operations. For existing SCA attacks, comprehending such internal operations requires heavyweight program analysis or manual efforts. This research proposes an attack framework to reconstruct private user images processed by media software via system side channels. The framework forms an effective workflow by incorporating convolutional networks, variational autoencoders, and generative adversarial networks.

Our evaluation of two popular side channels shows that the reconstructed images consistently match user inputs, making privacy leakage attacks more practical. We also show surprising results that even one-bit data read/write pattern side channels, which are deemed minimally informative, can be used to reconstruct quality images using our framework.

论文标题：

Private Image Reconstruction from System Side Channels Using Generative Models

论文链接：

https://openreview.net/forum?id=y06VOYLcQXa

简翔沄：约翰霍普金斯大学脑与心理研究所的博士班学生。

报告题目：

MAPPING THE TIMESCALE ORGANIZATION OF NEURAL LANGUAGE MODELS

摘要：

In the human brain, sequences of language input are processed within a distributed

and hierarchical architecture, in which higher stages of processing encode

contextual information over longer timescales. In contrast, in recurrent neural

networks which perform natural language processing, we know little about

how the multiple timescales of contextual information are functionally organized.

Therefore, we applied tools developed in neuroscience to map the “processing

timescales” of individual units within a word-level LSTM language model. This

timescale-mapping method assigned long timescales to units previously found to

track long-range syntactic dependencies. Additionally, the mapping revealed a

small subset of the network (less than 15% of units) with long timescales and

whose function had not previously been explored. We next probed the functional

organization of the network by examining the relationship between the processing

timescale of units and their network connectivity. We identified two classes of

long-timescale units: “controller” units composed a densely interconnected subnetwork

and strongly projected to the rest of the network, while “integrator” units

showed the longest timescales in the network, and expressed projection profiles

closer to the mean projection profile. Ablating integrator and controller units affected

model performance at different positions within a sentence, suggesting distinctive

functions of these two sets of units. Finally, we tested the generalization

of these results to a character-level LSTM model and models with different architectures.

In summary, we demonstrated a model-free technique for mapping the

timescale organization in recurrent neural networks, and we applied this method

to reveal the timescale and functional organization of neural language models.

论文标题：

MAPPING THE TIMESCALE ORGANIZATION OF NEURAL LANGUAGE MODELS

论文链接：

https://arxiv.org/abs/2012.06717

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