Ramin Moslemi is a former researcher at NEC Laboratories America, Inc.

Posts

Learning to Learn across Diverse Data Biases in Deep Face Recognition

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Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data. However, the data used for training CNNs is often imbalanced. Prior works largely focus on the long-tailed nature of face datasets in data volume per identity or focus on single bias variation. In this paper, we show that many bias variations such as ethnicity, head pose, occlusion and blur can jointly affect the accuracy significantly. We propose a sample level weighting approach termed Multi-variation Cosine Margin (MvCoM), to simultaneously consider the multiple variation factors, which orthogonally enhances the face recognition losses to incorporate the importance of training samples. Further, we leverage a learning to learn approach, guided by a held-out meta learning set and use an additive modeling to predict the MvCoM. Extensive experiments on challenging face recognition benchmarks demonstrate the advantages of our method in jointly handling imbalances due to multiple variations.

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Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction

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Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute actions. Our work addresses two key challenges in trajectory prediction, learning multimodal outputs, and better predictions by imposing constraints using driving knowledge. Recent methods have achieved strong performances using Multi-Choice Learning objectives like winner-takes-all (WTA) or best-of-many. But the impact of those methods in learning diverse hypotheses is under-studied as such objectives highly depend on their initialization for diversity. As our first contribution, we propose a novel Divide-And-Conquer (DAC) approach that acts as a better initialization technique to WTA objective, resulting in diverse outputs without any spurious modes. Our second contribution is a novel trajectory prediction framework called ALAN that uses existing lane centerlines as anchors to provide trajectories constrained to the input lanes. Our framework provides multi-agent trajectory outputs in a forward pass by capturing interactions through hypercolumn descriptors and incorporating scene information in the form of rasterized images and per-agent lane anchors. Experiments on synthetic and real data show that the proposed DAC captures the data distribution better compare to other WTA family of objectives. Further, we show that our ALAN approach provides on par or better performance with SOTA methods evaluated on Nuscenes urban driving benchmark.

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Stochastic Decision-Making Model for Aggregation of Residential Units with PV-Systems and Storages

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Many residential energy consumers have installed photovoltaic (PV) panels and energy storage systems. These residential users can aggregate and participate in the energy markets. A stochastic decision making model for an aggregation of these residential units for participation in two-settlement markets is proposed in this paper. Scenarios are generated using Seasonal Autoregressive Integrated Moving Average (SARIMA) model and joint probability distribution function of the forecast errors to model the uncertainties of the real-time prices, PV generations and demands. The proposed scenario generation model of this paper treats forecast errors as random variable, which allows to reflect new information observed in the real-time market into scenario generation process without retraining SARIMA or re-fitting probability distribution functions over the forecast errors. This approach significantly improves the computational time of the proposed model. A simulation study is conducted for an aggregation of 6 residential units, and the results highlights the benefits of aggregation as well as the proposed stochastic decision-making model.

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VeCharge: Intelligent Energy Management for Electric Vehicle charging

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2018’s 1.2 million North American charging ports will grow ten times to over 12.6 million by 2027, according to Navigant, which could overwhelm the nation’s grids. DC Fast charging requires grid upgrade to supply the new charging demand. However, since the utilization ratio of those charging station is currently low. Demand charge cost can reach up to 90% of the total bill. Combining fast charging with energy storage can mitigate grid impacts and reduce demand charges. EV specific pricing is proposed for EV charging by many energy suppliers. Without managed charging, EV owner will lose the benefit of lowering charging cost by avoiding peak hour charging or missing the period when renewable energy generation is abundant.

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Aggregation of BTM Battery Storages to Provide Ancillary Services in Wholesale Electricity Markets

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The behind the meter battery energy storage systems (BTM-BESSs) have been deployed widely by indus-trial/commercial buildings to manage electricity transaction with utilities in order to reduce customers’ electricity bills. Commercial BTM battery storages are mainly employed to cut the customers’ monthly demand peaks, which is rewarded by significant decrease in the monthly demand charge. However, given complexity of demand charge management problems, the rates of return on investments for installation of BTM-BESSs are not appealing enough. In this paper, an aggregation model for BTM-BESSs is proposed in order to provide the opportunity for the BTM-EMS units to participate in the multiple wholesale markets to provide ancillary services, in addition to the demand charge management, to maximize owners’ payoff from installation of BTM-BESSs. Finally, the efficiency of the proposed aggregation model is validated through the simulation studies on the real value data.

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Learning from Rules Performs as Implicit Regularization

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In this paper, we study the generalization performance of deep neural networks in learning problems where the given task is governed by a set of rules. We consider two settings of supervised learning and rule-based learning. In supervised learning, the network is trained with pairs of inputs and the corresponding solutions that satisfy the problem constraints. In rule-based learning, the constraints are encoded into a neural network module that is applied on the output of the solver network. In this approach, instead of training with any actual solutions of the problem, the model will be trained to explicitly satisfy the constraints. We perform the experiments on two problems of solving a system of nonlinear equations and solving Sudoku puzzles. Our experimental results show that, compared to supervised approach, rule-based learning results in higher training error, but significantly lower validation error, especially when training data is small, thus performing as an implicit regularization.

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Transactive Energy Management with Blockchain Smart Contracts for P2P Multi-Settlement Markets

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Integration of renewables and energy storage, leading to rise of prosumers, has created localized bidirectional flows. As the result, the utility demand has decreased and traditional centralized controller can no longer realize the optimal performance of ever growing distribution systems. To achieve scalable control, exploiting the potential of smart loads and Distributed Energy Resource (DER) controllability, a framework for decentralized Peer-To-Peer (P2P) energy management has been developed to manage localized micro-energy markets. Such decentralized management approach could, in theory, sustain diverse prosumer and utility business models. We have been developing an autonomous decentralized management solution that maximizes the benefit of prosumers while protecting utility assets. This P2P energy trading market leverages Blockchain technology and its Smart Contract framework. This paper presents 1) transactive energy market for P2P multi-settlement markets, 2) architecture of blockchain-based energy management system, 3) smart contract design that solves an economic dispatch problem of DERs to maximize the profit of pro/consumers.

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