Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is fragmented when it comes to datasets and standardized evaluation techniques. To facilitate progress in this field, we introduce the Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology. We curate tasks into specific training, validation, and test splits to ensure that each task tests biologically relevant generalization that transfers to real-life scenarios. We benchmark a range of approaches to semi-supervised protein representation learning, which span recent work as well as canonical sequence learning techniques. We find that self-supervised pretraining is helpful for almost all models on all tasks, more than doubling performance in some cases. Despite this increase, in several cases features learned by self-supervised pretraining still lag behind features extracted by state-of-the-art non-neural techniques. This gap in performance suggests a huge opportunity for innovative architecture design and improved modeling paradigms that better capture the signal in biological sequences. TAPE will help the machine learning community focus effort on scientifically relevant problems.

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both the communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this paper, we propose a joint computation offloading and multi-user scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multi-user resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semi-distributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. Simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

This article describes REBA, a knowledge representation and reasoning architecture for robots that is based on tightly-coupled transition diagrams of the domain at two different levels of granularity. An action language is extended to support non-boolean fluents and non-deterministic causal laws, and used to describe the domain's transition diagrams, with the fine-resolution transition diagram being defined as a refinement of the coarse-resolution transition diagram. The coarse-resolution system description, and a history that includes prioritized defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. The zoomed fine-resolution system description, and a probabilistic representation of the uncertainty in sensing and actuation, are used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions. The fine-resolution outcomes of executing these concrete actions are used to infer coarse-resolution outcomes that are added to the coarse-resolution history and used for subsequent coarse-resolution reasoning. The architecture thus combines the complementary strengths of declarative programming and probabilistic graphical models to represent and reason with non-monotonic logic-based and probabilistic descriptions of uncertainty and incomplete domain knowledge. In addition, we describe a general methodology for the design of software components of a robot based on these knowledge representation and reasoning tools, and provide a path for proving the correctness of these components. The architecture is evaluated in simulation and on a mobile robot finding and moving target objects to desired locations in indoor domains, to show that the architecture supports reliable and efficient reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.

As Machine Learning (ML) becomes pervasive in various real world systems, the need for models to be interpretable or explainable has increased. We focus on interpretability, noting that models often need to be constrained in size for them to be considered understandable, e.g., a decision tree of depth 5 is easier to interpret than one of depth 50. This suggests a trade-off between interpretability and accuracy. We propose a technique to minimize this tradeoff. Our strategy is to first learn a powerful, possibly black-box, probabilistic model on the data, which we refer to as the oracle. We use this to adaptively sample the training dataset to present data to our model of interest to learn from. Determining the sampling strategy is formulated as an optimization problem that, independent of the dimensionality of the data, uses only seven variables. We empirically show that this often significantly increases the accuracy of our model. Our technique is model agnostic - in that, both the interpretable model and the oracle might come from any model family. Results using multiple real world datasets, using Linear Probability Models and Decision Trees as interpretable models, and Gradient Boosted Model and Random Forest as oracles are presented. Additionally, we discuss an interesting example of using a sentence-embedding based text classifier as an oracle to improve the accuracy of a term-frequency based bag-of-words linear classifier.

We introduce a framework for approximate analysis of Markov decision processes (MDP) with bounded-, unbounded-, and infinite-horizon properties. The main idea is to identify a ``core'' of an MDP, i.e., a subsystem where we provably remain with high probability, and to avoid computation on the less relevant rest of the state space. Although we identify the core using simulations and statistical techniques, it allows for rigorous error bounds in the analysis. Consequently, we obtain efficient analysis algorithms based on partial exploration for various settings, including the challenging case of strongly connected systems.

According to Thomas Edison, g, for example a fragrance composed of 700 g of "ingredient "Genius is one percent inspiration and 99 percent A" and 300 g of "ingredient B". A fragrance can have desired perspiration" is an example. In many situations, properties related to aromatics (e.g., the type of smell), researchers and inventors already have a variety popularity (e.g., frequent patterns of ingredient combinations, of data and manage to create something new or combinations that should be avoided), and appropriateness by using it, but the key problem is how to select for certain use cases (e.g., combinations for perfumes, shampoos, and combine knowledge. In this paper, we propose or hand soaps). Perfumers who create new fragrances a new Markov chain Monte Carlo (MCMC) algorithm seek to develop various fragrances with desired properties. It to generate composition ratios, nonnegativeinteger-valued is also possible that perfumers are willing to accept certain vectors with two properties: (i) the fragrances lacking some desired properties, because they can sum of the elements of each vector is constant, and still draw inspiration from such fragrances. Thus, it is interesting (ii) only a small number of elements is nonzero.

We study the problem of learning graphical models with latent variables. We give the first algorithm for learning locally consistent (ferromagnetic or antiferromagnetic) Restricted Boltzmann Machines (or RBMs) with {\em arbitrary} external fields. Our algorithm has optimal dependence on dimension in the sample complexity and run time however it suffers from a sub-optimal dependency on the underlying parameters of the RBM. Prior results have been established only for {\em ferromagnetic} RBMs with {\em consistent} external fields (signs must be same)\cite{bresler2018learning}. The proposed algorithm strongly relies on the concavity of magnetization which does not hold in our setting. We show the following key structural property: even in the presence of arbitrary external field, for any two observed nodes that share a common latent neighbor, the covariance is high. This enables us to design a simple greedy algorithm that maximizes covariance to iteratively build the neighborhood of each vertex.

We propose to learn deep undirected graphical models (i.e., MRFs), with a non-ELBO objective for which we can calculate exact gradients. In particular, we optimize a saddle-point objective deriving from the Bethe free energy approximation to the partition function. Unlike much recent work in approximate inference, the derived objective requires no sampling, and can be efficiently computed even for very expressive MRFs. We furthermore amortize this optimization with trained inference networks. Experimentally, we find that the proposed approach compares favorably with loopy belief propagation, but is faster, and it allows for attaining better held out log likelihood than other recent approximate inference schemes.

ABSTRACT This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information. The framework sources and extracts multisource data generated from construction operations, which provides rich information for input modeling. The framework implements Bayesian deep neural networks to facilitate the purpose of incorporating richer information in input modeling. A case study on road paving operation is performed to test the feasibility and applicability of the proposed framework. Overall, this research enhances input modeling by deriving detailed input models, thereby, augmenting the decision-making processes in construction operations.

Building performance discrepancies between building design and operation are one of the causes that lead many new designs fail to achieve their goals and objectives. One of main factors contributing to the discrepancy is occupant behaviors. Occupants responding to a new design are influenced by several factors. Existing building performance models (BPMs) ignore or partially address those factors (called contextual factors) while developing BPMs. To potentially reduce the discrepancies and improve the prediction accuracy of BPMs, this paper proposes a computational framework for learning mixture models by using Generative Adversarial Networks (GANs) that appropriately combining existing BPMs with knowledge on occupant behaviors to contextual factors in new designs. Immersive virtual environments (IVEs) experiments are used to acquire data on such behaviors. Performance targets are used to guide appropriate combination of existing BPMs with knowledge on occupant behaviors. The resulting model obtained is called an augmented BPM. Two different experiments related to occupant lighting behaviors are shown as case study. The results reveal that augmented BPMs significantly outperformed existing BPMs with respect to achieving specified performance targets. The case study confirms the potential of the computational framework for improving prediction accuracy of BPMs during design.