Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model

• URL: http://arxiv.org/abs/2108.07139v1
• Date: Mon, 16 Aug 2021 15:14:55 GMT
• Title: Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model
• Authors: Souridas Alaka, Rishikesh Sreekumar, Hrithwik Shalu
• Abstract summary: This study investigates the use of adaptive (learnable) embeddings to represent inter-related features. The data used for this study is collected from a classical T20 tournament IPL (Indian Premier League)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data analysis has become a necessity in the modern era of cricket. Everything from effective team management to match win predictions use some form of analytics. Meaningful data representations are necessary for efficient analysis of data. In this study we investigate the use of adaptive (learnable) embeddings to represent inter-related features (such as players, teams, etc). The data used for this study is collected from a classical T20 tournament IPL (Indian Premier League). To naturally facilitate the learning of meaningful representations of features for accurate data analysis, we formulate a deep representation learning framework which jointly learns a custom set of embeddings (which represents our features of interest) through the minimization of a contrastive loss. We base our objective on a set of classes obtained as a result of hierarchical clustering on the overall run rate of an innings. It's been assessed that the framework ensures greater generality in the obtained embeddings, on top of which a task based analysis of overall run rate prediction was done to show the reliability of the framework.

Related papers

• A Differentiable Adversarial Framework for Task-Aware Data Subsampling [0.5371337604556311]
  We introduce the antagonistic soft selection subsampling (ASSS) framework as a novel paradigm that reconstructs data reduction into a differentiable end-to-end learning problem.<n>This work establishes task aware data subsampling as a learnable component, providing a principled solution for effective large-scale data learning.
  arXiv  Detail & Related papers  (2026-01-05T13:10:09Z)
• SPaRFT: Self-Paced Reinforcement Fine-Tuning for Large Language Models [51.74498855100541]
  Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL)<n>We propose textbfSPaRFT, a self-paced learning framework that enables efficient learning based on the capability of the model being trained.
  arXiv  Detail & Related papers  (2025-08-07T03:50:48Z)
• Intrinsic User-Centric Interpretability through Global Mixture of Experts [31.738009841932374]
  InterpretCC is a family of intrinsically interpretable neural networks that optimize for ease of human understanding and explanation faithfulness.<n>We show that InterpretCC explanations are found to have higher actionability and usefulness over other intrinsically interpretable approaches.
  arXiv  Detail & Related papers  (2024-02-05T11:55:50Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model [2.14986347364539]
  We developed an automated short answer grading model that provided both analytic scores and holistic scores. The accuracy and quadratic weighted kappa of our model were 0.67 and 0.71 on a subset of the publicly available ASAG dataset.
  arXiv  Detail & Related papers  (2023-05-29T22:05:29Z)
• The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning [32.15608637930748]
  We show that there exists a trade-off between the two desiderata so that one may not be able to achieve both simultaneously. We provide analysis using a theoretical data model and show that, while more diverse pre-training data result in more diverse features for different tasks, it puts less emphasis on task-specific features.
  arXiv  Detail & Related papers  (2023-02-28T22:14:33Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice [52.11183787786718]
  Fine-tuning a pre-trained model on the target data is widely used in many deep learning applications. Recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy. We propose a novel selection strategy to select a subset from pre-training data to help improve the generalization on the target task.
  arXiv  Detail & Related papers  (2021-11-24T06:18:32Z)
• Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data [85.43008636875345]
  We show that diverse representation in training data is key to increasing subgroup performances and achieving population level objectives. Our analysis and experiments describe how dataset compositions influence performance and provide constructive results for using trends in existing data, alongside domain knowledge, to help guide intentional, objective-aware dataset design.
  arXiv  Detail & Related papers  (2021-03-05T00:27:08Z)
• Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification [17.237331828747006]
  This work focuses on few-shot relation classification (FSRC) We propose an adaptive mixture mechanism to add label words to the representation of the class prototype. Experiments have been conducted on FewRel under different few-shot (FS) settings.
  arXiv  Detail & Related papers  (2021-01-10T11:25:42Z)
• Self-Supervision based Task-Specific Image Collection Summarization [3.115375810642661]
  We propose a novel approach to task-specific image corpus summarization using semantic information and self-supervision. Our method uses a classification-based Wasserstein generative adversarial network (WGAN) as a feature generating network. The model then generates a summary at inference time by using K-means clustering in the semantic embedding space.
  arXiv  Detail & Related papers  (2020-12-19T10:58:04Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)
• ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning [85.33459673197149]
  We introduce a new Reading dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations. In this paper, we propose to identify biased data points and separate them into EASY set and the rest as HARD set. Empirical results show that state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set. However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models.
  arXiv  Detail & Related papers  (2020-02-11T11:54:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.