The use of machine learning and intelligent systems has become an established practice in the realm of malware detection and cyber threat prevention. In an environment characterized by widespread accessibility and big data, the feasibility of malware classification without the use of artificial intelligence-based techniques has been diminished exponentially. Also characteristic of the contemporary realm of automated, intelligent malware detection is the threat of adversarial machine learning. Adversaries are looking to target the underlying data and/or algorithm responsible for the functionality of malware classification to map its behavior or corrupt its functionality. The ends of such adversaries are bypassing the cyber security measures and increasing malware effectiveness. The focus of this research is the design of an intelligent systems approach using machine learning that can accurately and robustly classify malware under adversarial conditions. Such an outcome ultimately relies on increased flexibility and adaptability to build a model robust enough to identify attacks on the underlying algorithm.

At an early age, human infants are able to learn and build a model of the world very quickly by constantly observing and interacting with objects around them. One of the most fundamental intuitions human infants acquire is intuitive physics. Human infants learn and develop these models, which later serve as prior knowledge for further learning. Inspired by such behaviors exhibited by human infants, we introduce a graphical physics network integrated with deep reinforcement learning. Specifically, we introduce an intrinsic reward normalization method that allows our agent to efficiently choose actions that can improve its intuitive physics model the most. Using a 3D physics engine, we show that our graphical physics network is able to infer object's positions and velocities very effectively, and our deep reinforcement learning network encourages an agent to improve its model by making it continuously interact with objects only using intrinsic motivation. We experiment our model in both stationary and non-stationary state problems and show benefits of our approach in terms of the number of different actions the agent performs and the accuracy of agent's intuition model. Videos are at https://www.youtube.com/watch?v=pDbByp91r3M&t=2s

We have developed the concept of pathway assembly to explore the amount of extrinsic information required to build an object. To quantify this information in an agnostic way, we present a method to determine the amount of pathway assembly information contained within such an object by deconstructing the object into its irreducible parts, and then evaluating the minimum number of steps to reconstruct the object along any pathway. The mathematical formalisation of this approach uses an assembly space. By finding the minimal number of steps contained in the route by which the objects can be assembled within that space, we can compare how much information (I) is gained from knowing this pathway assembly index (PA) according to I_PA=log (|N|)/(|N_PA |) where, for an end product with PA=x, N is the set of objects possible that can be created from the same irreducible parts within x steps regardless of PA, and NPA is the subset of those objects with the precise pathway assembly index PA=x. Applying this formalism to objects formed in 1D, 2D and 3D space allows us to identify objects in the world or wider Universe that have high assembly numbers. We propose that objects with PA greater than a threshold are important because these are uniquely identifiable as those that must have been produced by biological or technological processes, rather than the assembly occurring via unbiased random processes alone. We think this approach is needed to help identify the new physical and chemical laws needed to understand what life is, by quantifying what life does.

Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperparameter determination process is necessary due to the large number of flexible hyperparameters. This work proposes AutoSlim, an automatic structured pruning framework with the following key performance improvements: (i) effectively incorporate the combination of structured pruning schemes in the automatic process; (ii) adopt the state-of-art ADMM-based structured weight pruning as the core algorithm, and propose an innovative additional purification step for further weight reduction without accuracy loss; and (iii) develop effective heuristic search method enhanced by experience-based guided search, replacing the prior deep reinforcement learning technique which has underlying incompatibility with the target pruning problem. Extensive experiments on CIFAR-10 and ImageNet datasets demonstrate that AutoSlim is the key to achieve ultra-high pruning rates on the number of weights and FLOPs that cannot be achieved before. As an example, AutoSlim outperforms the prior work on automatic model compression by up to 33$\times$ in pruning rate under the same accuracy. We release all models of this work at anonymous link: http://bit.ly/2VZ63dS.

Scene text magnifier aims to magnify text in natural scene images without recognition. It could help the special groups, who have myopia or dyslexia to better understand the scene. In this paper, we design the scene text magnifier through interacted four CNN-based networks: character erasing, character extraction, character magnify, and image synthesis. The architecture of the networks are extended based on the hourglass encoder-decoders. It inputs the original scene text image and outputs the text magnified image while keeps the background unchange. Intermediately, we can get the side-output results of text erasing and text extraction. The four sub-networks are first trained independently and fine-tuned in end-to-end mode. The training samples for each stage are processed through a flow with original image and text annotation in ICDAR2013 and Flickr dataset as input, and corresponding text erased image, magnified text annotation, and text magnified scene image as output. To evaluate the performance of text magnifier, the Structural Similarity is used to measure the regional changes in each character region. The experimental results demonstrate our method can magnify scene text effectively without effecting the background.

Recently, there has been increasing interest in developing distributed machine learning algorithms. Notable examples include distributed linear regression [1], multi-arm bandit [2], reinforcement learning (RL) [3], and deep learning [4]. Such algorithms have promising applications in large-scale networks, such as social platforms, online economic networks, cyber-physical systems, and Internet of Things, primarily because in such a complex network, it is impossible to collect all the information at the same point and each component of the network may not be willing to share its private information due to privacy issues. Multi-agent reinforcement learning (MARL) problems have recently received increasing attention. In general, MARL problems are investigated in settings that are either collaborative, competitive, or a mixture of the two. For collaborative MARL, the most rudimentary framework is the canonical multi-agent Markov decision process [5, 6], where the agents share a common reward function that is determined by the joint actions of all agents. Another notable framework for collaborative MARL is the team Markov game model, also with a shared reward function among agents [7, 8]. These two frameworks were then extended to the setting where agents are allowed to have heterogeneous reward functions[3,9-12], collaborating with the goal of maximizing the long-term return corresponding to the team averaged reward.

Deep learning models are now used in many different industries, while in certain domains safety is not a critical issue in the medical field it is a huge concern. Not only, we want the models to generalize well but we also want to know the models confidence respect to its decision and which features matter the most. Our team aims to develop a full pipeline in which not only displays the uncertainty of the models decision but also, the saliency map to show which sets of pixels of the input image contribute most to the predictions.

Statistical inference over structured instances of dependent variables (e.g., labeled sequences, trees, or general graphs) is a fundamental problem in many areas. Examples include computer vision (Nowozin et al., 2011; Dollár & Zitnick, 2013; Chen et al., 2018), natural language processing (Huang et al., 2015; Hu et al., 2016), and computational biology (Li et al., 2007). In many practical setups (Shin et al., 2015; Rekatsinas et al., 2017; Sa et al., 2019; Heidari et al., 2019b), inference problems involve noisy observations of discrete labels assigned to the nodes and edges of a given structured instance and the goal is to infer a labeling of the vertices that achieves low disagreement rate between the correct ground truth labels Y and the predicted labels Ŷ, i.e., low Hamming error. We refer to this problem as statistical recovery. Our motivation to study the problem of statistical recovery stems from our recent work on data cleaning (Rekatsinas et al., 2017; Sa et al., 2019; Heidari et al., 2019b). This work introduces HoloClean, a state-of-the-art inference engine for data curation that casts data cleaning as a structured prediction problem (Sa et al., 2019): Given a dataset as input, it associates each of its cells with a random variable, and uses logical integrity constraints over this dataset (e.g., key constraints or functional dependencies) to introduce dependencies over these random variables. The labels that each random variable can take are determined by the domain of the attribute associated with the corresponding cell. Since we focus on data cleaning, the input dataset corresponds to a noisy version of the latent, clean dataset. Our goal is to recover the latter.

The recent empirical success of unsupervised cross-domain mapping algorithms, between two domains that share common characteristics, is not well-supported by theoretical justifications. This lacuna is especially troubling, given the clear ambiguity in such mappings. We work with the adversarial training method called the Wasserstein GAN and derive a novel generalization bound, which limits the risk between the learned mapping $h$ and the target mapping $y$, by a sum of two terms: (i) the risk between $h$ and the most distant alternative mapping that was learned by the same cross-domain mapping algorithm, and (ii) the minimal Wasserstein GAN divergence between the target domain and the domain obtained by applying a hypothesis $h^*$ on the samples of the source domain, where $h^*$ is a hypothesis selected by the same algorithm. The bound is directly related to Occam's razor and encourages the selection of the minimal architecture that supports a small Wasserstein GAN divergence. The bound leads to multiple algorithmic consequences, including a method for hyperparameters selection and for an early stopping in cross-domain mapping GANs. We also demonstrate a novel capability for unsupervised learning of estimating confidence in the mapping of every specific sample. Lastly, we show how non-minimal architectures can be effectively trained by an inverted knowledge distillation, in which a minimal architecture is used to train a larger one, leading to higher quality outputs.

Machine learning approaches for building task-oriented dialogue systems require large conversational datasets with labels to train on. We are interested in building task-oriented dialogue systems from human-human conversations, which may be available in ample amounts in existing customer care center logs or can be collected from crowd workers. Annotating these datasets can be prohibitively expensive. Recently multiple annotated task-oriented human-machine dialogue datasets have been released, however their annotation schema varies across different collections, even for well-defined categories such as dialogue acts (DAs). We propose a Universal DA schema for task-oriented dialogues and align existing annotated datasets with our schema. Our aim is to train a Universal DA tagger (U-DAT) for task-oriented dialogues and use it for tagging human-human conversations. We investigate multiple datasets, propose manual and automated approaches for aligning the different schema, and present results on a target corpus of human-human dialogues. In unsupervised learning experiments we achieve an F1 score of 54.1% on system turns in human-human dialogues. In a semi-supervised setup, the F1 score increases to 57.7% which would otherwise require at least 1.7K manually annotated turns. For new domains, we show further improvements when unlabeled or labeled target domain data is available.