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Please enter a description about the method here

In this paper we aim to answer questions based on images when provided with a dataset of question-answer pairs for a number of images during training. A number of methods have focused on solving this problem by using image based attention. This is done by focusing on a specific part of the image while answering the question. Humans also do so when solving this problem. However, the regions that the previous systems focus on are not correlated with the regions that humans focus on. The accuracy is limited due to this drawback. In this paper, we propose to solve this problem by using an exemplar based method. We obtain one or more supporting and opposing exemplars to obtain a differential attention region. This differential attention is closer to human attention than other image based attention methods. It also helps in obtaining improved accuracy when answering questions. The method is evaluated on challenging benchmark datasets. We perform better than other image based attention methods and are competitive with other state of the art methods that focus on both image and questions.

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In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of obtaining word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder. This discriminator is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validate our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and provide competitive results on the paraphrase generation and sentiment analysis task on standard dataset. These results are also shown to be statistically significant. Github Link:https://github.com/dev-chauhan/PQG-pytorch. 2 The PQG dataset is available on this link: https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs. 3 website: https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs. 4 we report same baseline results as mentioned in [10] 5 website: www.kaggle.com/c/sentiment-analysis-on-movie-reviews. 6 Code: https://github.com/dev-chauhan/PQG-pytorch.

EncAttAgg introduced two attenders to tackle two problems: 1) We introduce a mutual attender layer to efficiently obtain the entity-pair-specific mention representations. 2) We introduce an integration attender to weight mention pairs of a target entity pair.

In this paper, we aim to obtain improved attention for a visual question answering (VQA) task. It is challenging to provide supervision for attention. An observation we make is that visual explanations as obtained through class activation mappings (specifically Grad-CAM) that are meant to explain the performance of various networks could form a means of supervision. However, as the distributions of attention maps and that of Grad-CAMs differ, it would not be suitable to directly use these as a form of supervision. Rather, we propose the use of a discriminator that aims to distinguish samples of visual explanation and attention maps. The use of adversarial training of the attention regions as a two-player game between attention and explanation serves to bring the distributions of attention maps and visual explanations closer. Significantly, we observe that providing such a means of supervision also results in attention maps that are more closely related to human attention resulting in a substantial improvement over baseline stacked attention network (SAN) models. It also results in a good improvement in rank correlation metric on the VQA task. This method can also be combined with recent MCB based methods and results in consistent improvement. We also provide comparisons with other means for learning distributions such as based on Correlation Alignment (Coral), Maximum Mean Discrepancy (MMD) and Mean Square Error (MSE) losses and observe that the adversarial loss outperforms the other forms of learning the attention maps. Visualization of the results also confirms our hypothesis that attention maps improve using this form of supervision.

This paper introduces the fast adaptive stochastic function accelerator (FASFA) for gradient-based optimization of stochastic objective functions. It works based on Nesterov-enhanced first and second momentum estimates. The method is simple and effective during implementation because it has intuitive/familiar hyperparameterization. The training dynamics can be progressive or conservative depending on the decay rate sum. It works well with a low learning rate and mini batch size. Experiments and statistics showed convincing evidence that FASFA could be an ideal candidate for optimizing stochastic objective functions, particularly those generated by multilayer perceptrons with convolution and dropout layers. In addition, the convergence properties and regret bound provide results aligning with the online convex optimization framework. In a first of its kind, FASFA addresses the growing need for diverse optimizers by providing next-generation training dynamics for artificial intelligence algorithms. Future experiments could modify FASFA based on the infinity norm.

FlexFlow is a deep learning engine that uses guided randomized search of the SOAP (Sample, Operator, Attribute, and Parameter) space to find a fast parallelization strategy for a specific parallel machine. To accelerate this search, FlexFlow introduces a novel execution simulator that can accurately predict a parallelization strategy’s performance and is three orders of magnitude faster than prior approaches that execute each strategy. FlexFlow uses two main components: a fast, incremental execution simulator to evaluate different parallelization strategies, and a Markov Chain Monte Carlo (MCMC) search algorithm that takes advantage of the incremental simulator to rapidly explore the large search space.

The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive PID control using two different approaches: Genetic Algorithms (GA-PID) and then Neural Networks (NN-PID) respectively. The vehicle nonlinear longitudinal dynamics are modeled using Powertrain blockset library. Finally, simulations are performed to assess and compare the performance of the two controllers subject to external disturbances.

Gaussian Affinity is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a Gaussian function: $$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = e^{\mathbb{x^{T}\_{i}}\mathbb{x\_{j}}} $$ Here $\mathbb{x^{T}\_{i}}\mathbb{x\_{j}}$ is dot-product similarity.

The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interaction, and in the incremental object learning. The novelty of our approach is the focus on natural, heterogeneous, and multimodal HRIs to incrementally learn new object models.

HetPipe is a hybrid parallel method that integrates pipelined model parallelism (PMP) with data parallelism (DP). In HetPipe, a group of multiple GPUs, called a virtual worker, processes minibatches in a pipelined manner, and multiple such virtual workers employ data parallelism for higher performance.

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Local Augmentation for Graph Neural Networks, or LA-GNN, is a data augmentation technique that enhances node features by its local subgraph structures. Specifically, it learns the conditional distribution of the connected neighbors’ representations given the representation of the central node, which has an analogy with the Skip-gram of word2vec model that predicts the probability of the context given the central word. After augmenting the neighborhood, we concat the initial and the generated feature matrix as input for GNNs.

Cyclical log annealing make harsh restarts by creating learning rate spikes to escape a local minimum on weight bias fields while retaining the capability to hang onto a low learning rate unlike cosine annealing, which opens up a discussion of if more greedy stochastic optimization schemes could be viable without as high of a risk of overfitting in late stage training. Though log annealing works similarly to cosine annealing, it's properties are different, as it's base learning rate is much more prevalent and much higher, though this can be adjusted by tweaking minimum decay and restart interval multipliers.

This study introduces Polar Lights Optimization (PLO), a metaheuristic algorithm inspired by the aurora phenomenon or polar lights. The aurora is a unique natural spectacle that occurs when energetic particles from the solar wind converge at the Earth's poles, influenced by the geomagnetic field and the Earth's atmosphere. By analyzing the motion of high-energy particles and delving into the underlying principles of physics, we propose a unique model for mimicking particle motion. This model integrates both gyration motion and aurora oval walk, with the former facilitating local exploitation while the latter enabling global exploration. By synergistically combining these two strategies, PLO achieves a better balance between local exploitation and global exploration. Additionally, a particle collision strategy is introduced to enhance the efficiency of escaping local optima. To evaluate the performance of PLO, a qualitative analysis experiment is designed to assess its ability to explore the problem space and search for solutions. PLO is compared with nine classic algorithms and eight high-performance algorithms using 30 benchmark functions from IEEE CEC2014. Furthermore, we compare and analyze PLO with the current state-of-the-art methods in the field, utilizing 12 benchmark functions from IEEE CEC2022. Subsequently, PLO is successfully applied to multi-threshold image segmentation and feature selection. Specifically, a PLO-based multi-threshold segmentation model and a binary PLO-based feature selection method are developed. The performance of PLO is also evaluated using 10 images from the Invasive Ductal Carcinoma (IDC) medical dataset, while the overall adaptability and accuracy of the feature selection model are tested using 8 medical datasets. These results affirm the emergence of PLO as an effective optimization tool ready for solving real-world problems, including those in the medical field. The source codes of PLO are available in https://aliasgharheidari.com/PLO.html

Recent deep neural network models trained on smaller and less diverse datasets use data augmentation to alleviate limitations such as overfitting, reduced robustness, and lower generalization. Methods using 3D datasets are among the most common to use data augmentation techniques such as random point drop, scaling, translation, rotations, and jittering. However, these data augmentation techniques are fixed and are often applied to the entire object, ignoring the object’s local geometry. Different local neighborhoods on the object surface hold a different amount of geometric complexity. Applying the same data augmentation techniques at the object level is less effective in augmenting local neighborhoods with complex structures. This paper presents PatchAugment, a data augmentation framework to apply different augmentation techniques to the local neighborhoods. Our experimental studies on PointNet++ and DGCNN models demonstrate the effectiveness of PatchAugment on the task of 3D Point Cloud Classification. We evaluated our technique against these models using four benchmark datasets, ModelNet40 (synthetic), ModelNet10 (synthetic), SHREC’16 (synthetic), and ScanObjectNN (real-world). [ICCVW 2021] PatchAugment: Local Neighborhood Augmentation in Point Cloud Classification. [Code]