Reinforcement Learning for Assignment Problem with Time Constraints

We present an end-to-end framework for the Assignment Problem with multiple tasks mapped to a group of workers, using reinforcement learning while preserving many constraints. Tasks and workers have time constraints and there is a cost associated with assigning a worker to a task. Each worker can perform multiple tasks until it exhausts its allowed time units (capacity). We train a reinforcement learning agent to find near optimal solutions to the problem by minimizing total cost associated with the assignments while maintaining hard constraints. We use proximal policy optimization to optimize model parameters. The model generates a sequence of actions in real-time which correspond to task assignment to workers, without having to retrain for changes in the dynamic state of the environment. In our problem setting reward is computed as negative of the assignment cost. We also demonstrate our results on bin packing and capacitated vehicle routing problem, using the same framework. Our results outperform Google OR-Tools using MIP and CP-SAT solvers with large problem instances, in terms of solution quality and computation time.

Identifying Linked Fraudulent Activities Using GraphConvolution Network

In this paper, we present a novel approach to identify linked fraudulent activities or actors sharing similar attributes, using Graph Convolution Network (GCN). These linked fraudulent activities can be visualized as graphs with abstract concepts like relationships and interactions, which makes GCNs an ideal solution to identify the graph edges which serve as links between fraudulent nodes. Traditional approaches like community detection require strong links between fraudulent attempts like shared attributes to find communities and the supervised solutions require large amount of training data which may not be available in fraud scenarios and work best to provide binary separation between fraudulent and non fraudulent activities. Our approach overcomes the drawbacks of traditional methods as GCNs simply learn similarities between fraudulent nodes to identify clusters of similar attempts and require much smaller dataset to learn. We demonstrate our results on linked accounts with both strong and weak links to identify fraud rings with high confidence. Our results outperform label propagation community detection and supervised GBTs algorithms in terms of solution quality and computation time.

Y-net: Biomedical Image Segmentation and Clustering

We propose a deep clustering architecture alongside image segmentation for medical image analysis. The main idea is based on unsupervised learning to cluster images on severity of the disease in the subject's sample, and this image is then segmented to highlight and outline regions of interest. We start with training an autoencoder on the images for segmentation. The encoder part from the autoencoder branches out to a clustering node and segmentation node. Deep clustering using Kmeans clustering is performed at the clustering branch and a lightweight model is used for segmentation. Each of the branches use extracted features from the autoencoder. We demonstrate our results on ISIC 2018 Skin Lesion Analysis Towards Melanoma Detection and Cityscapes datasets for segmentation and clustering. The proposed architecture beats UNet and DeepLab results on the two datasets, and has less than half the number of parameters. We use the deep clustering branch for clustering images into four clusters. Our approach can be applied to work with high complexity datasets of medical imaging for analyzing survival prediction for severe diseases or customizing treatment based on how far the disease has propagated. Clustering patients can help understand how binning should be done on real valued features to reduce feature sparsity and improve accuracy on classification tasks. The proposed architecture can provide an early diagnosis and reduce human intervention on labeling as it can become quite costly as the datasets grow larger. The main idea is to propose a one shot approach to segmentation with deep clustering.

AI Décor

Confused about renovating your space? Choosing the perfect color for your walls is always a challenging task. One does rounds of color consultation and several patch tests. This paper proposes an AI tool to pitch paint based on attributes of your room and other furniture, and visualize it on your walls. It makes the color selection process a whole lot easier. It basically takes in images of a room, detects furniture objects using YOLO object detection. Once these objects have been detected, the tool picks out color of the object. Later this object specific information gets appended to the room attributes (room_type, room_size, preferred_tone, etc) and a deep neural net is trained to make predictions for color/texture/wallpaper for the walls. Finally, these predictions are visualized on the walls from the images provided. The idea is to take the knowledge of a color consultant and pitch colors that suit the walls and provide a good contrast with the furniture and harmonize with different colors in the room. Transfer learning for YOLO object detection from the COCO dataset was used as a starting point and the weights were later fine-tuned by training on additional images. The model was trained on 1000 records listing the room and furniture attributes, to predict colors. Given the room image, this method finds the best color scheme for the walls. These predictions are then visualized on the walls in the image using image segmentation. The results are visually appealing and automatically enhance the color look-and-feel.

Predictive Image Regression for Longitudinal Studies with Missing Data

In this paper, we propose a predictive regression model for longitudinal images with missing data based on large deformation diffeomorphic metric mapping (LDDMM) and deep neural networks. Instead of directly predicting image scans, our model predicts a vector momentum sequence associated with a baseline image. This momentum sequence parameterizes the original image sequence in the LDDMM framework and lies in the tangent space of the baseline image, which is Euclidean. A recurrent network with long term-short memory (LSTM) units encodes the time-varying changes in the vector-momentum sequence, and a convolutional neural network (CNN) encodes the baseline image of the vector momenta. Features extracted by the LSTM and CNN are fed into a decoder network to reconstruct the vector momentum sequence, which is used for the image sequence prediction by deforming the baseline image with LDDMM shooting. To handle the missing images at some time points, we adopt a binary mask to ignore their reconstructions in the loss calculation. We evaluate our model on synthetically generated images and the brain MRIs from the OASIS dataset. Experimental results demonstrate the promising predictions of the spatiotemporal changes in both datasets, irrespective of large or subtle changes in longitudinal image sequences.