Neural networks made easy (Part 72): Trajectory prediction in noisy environments
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The noise prediction module solves the auxiliary problem of identifying noise in the analyzed trajectories. This helps the movement prediction model better model potential spatial diversity and improves understanding of the underlying representation in movement prediction, thereby improving future predictions.
The authors of the method conducted additional experiments to empirically demonstrate the critical importance of the spatial consistency and noise prediction modules for SSWNP. When using only the spatial consistency module to solve the movement prediction problem, suboptimal performance of the trained model is observed. Therefore, they integrate both modules in their work.
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Neural networks made easy (Part 73): AutoBots for predicting price movements
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The proposed method is based on the Encoder-Decoder architecture. It was developed to solve problems of safe control of robotic systems. It allows the generation of sequences of trajectories for multiple agents consistent with the scene. AutoBots can predict the trajectory of one ego-agent or the distribution of future trajectories for all agents in the scene. In our case, we will try to apply the proposed model to generate sequences of price movements of currency pairs consistent with market dynamics.
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Neural networks made easy (Part 74): Trajectory prediction with adaptation
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Building a trading strategy is inseparable from analyzing the market situation and forecasting the most likely movement of a financial instrument. This movement often correlated with other financial assets and macroeconomic indicators. This can be compared with the movement of transport, where each vehicle follows its own individual destination. However, their actions on the road are interconnected to a certain extent and are strictly regulated by traffic rules. Also, due to the individual perception of the road situation by vehicle drivers, a share of stochasticity remains on the roads.
In this article I want to introduce you to a method for effectively jointly predicting the trajectories of all agents on the scene with dynamic learning of weights
ADAPT, which was proposed to solve problems in the field of navigation of autonomous vehicles. The method was first presented in the article "
ADAPT: Efficient Multi-Agent Trajectory Prediction with Adaptation".
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Neural networks made easy (Part 75): Improving the performance of trajectory prediction models
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Forecasting the trajectory of the upcoming price movement probably plays one of the key roles in the process of constructing trading plans for the desired planning horizon. The accuracy of such forecasts is critical. In an attempt to improve the quality of trajectory forecasting, we complicate our trajectory forecasting models.
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Developing a multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status
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In the previous
article, we have significantly revised the code architecture to build a multi-currency EA with several parallel working strategies. Trying to achieve simplicity and clarity, we have so far only considered a certain minimum set of functionality. Even considering the limitations of our task, we have significantly altered the code from the previous
articles.
Now hopefully we have the groundwork that is sufficient enough to increase functionality without radical changes to the already written code. We will try to make a minimum number of edits only where it is really necessary.
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Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state
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Object detection in video has a number of certain characteristics and must solve the problem of changes in object features caused by motion, which are not encountered in the image domain. One of the solutions is to use temporal information and combine features from adjacent frames. The paper "
FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors" proposes a new approach to detecting objects in video. The authors of the article improve the quality of queries for Transformer-based models by aggregating them. To achieve this goal, a practical method is proposed to generate and aggregate queries according to the features of the input frames. Extensive experimental results provided in the paper validate the effectiveness of the proposed method. The proposed approaches can be extended to a wide range of methods for detecting objects in images and videos to improve their efficiency.
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Neural networks made easy (Part 80): Graph Transformer Generative Adversarial Model (GTGAN)
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The recently published paper "
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation" introduces the algorithm for the graph transformer generative adversarial model (
GTGAN), which succinctly combines both of these approaches. The authors of the GTGAN algorithm address the problem of creating a realistic architectural design of a house from an input graph. The generator model they presented consists of three components: a message passing convolutional neural network (
Conv-MPN), Graph Transformer encoder (GTE) and generation head.
Qualitative and quantitative experiments on three complex graphically constrained architectural layout generations with three datasets that were presented in the paper demonstrate that the proposed method can generate results superior to previously presented algorithms.
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Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)
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A particularly interesting method entitled CCMR was presented in the paper "
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning". It is an approach to optical flow estimation that combines the advantages of attention-oriented methods of motion aggregation concepts and high-resolution multi-scale approaches. The CCMR method consistently integrates context-based motion grouping concepts into a high-resolution coarse-grained estimation framework. This allows for detailed flow fields that also provide high accuracy in occluded areas. In this context, the authors of the method propose a two-stage motion grouping strategy where global self-attentional contextual features are first computed and them used to guide motion features iteratively across all scales. Thus, context-directed reasoning about
XCiT-based motion provides processing at all coarse-grained scales. Experiments conducted by the authors of the method demonstrate the strong performance of the proposed approach and the advantages of its basic concepts.
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Matrix Factorization: The Basics
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In this article, we will talk about matrix calculations. Dear readers, do not rush to refuse reading this article, thinking that we will talk about something purely mathematical and too complicated. Contrary to what many people think, a good programmer is not someone who writes a giant program that only they can understand, or someone who writes code in a trendy programming language. A real and good programmer understands that a computer is nothing more than a computing machine to which we can tell how computations should be performed. It doesn't matter what exactly we are creating, it can be a simple text editor that does not contain any mathematical code. But this is just an illusion. Even a text editor contains a fair amount of math in its code, especially if it has a spell checker built into it.
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Neural Networks Made Easy (Part 83): The "Conformer" Spatio-Temporal Continuous Attention Transformer Algorithm
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The unpredictability of financial market behavior can probably be compared to the volatility of the weather. However, humanity has done quite a lot in the field of weather forecasting. So, we can now quite trust the weather forecasts provided by meteorologists. Can we use their developments to forecast the "weather" in financial markets? In this article, we will get acquainted with the complex algorithm of the "Conformer" Spatio-Temporal Continuous Attention Transformer, which was developed for the purposes of weather forecasting and is presented in the paper "
Conformer: Embedding Continuous Attention in Vision Transformer for Weather Forecasting". In their work, the authors of the method propose the
Continuous Attention algorithm. They combine it with those we discussed in the previous article on
Neural ODE.
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