Add 'Viewpoint-Invariant Exercise Repetition Counting'

Viewpoint-Invariant-Exercise-Repetition-Counting.md

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+<br> We practice our model by minimizing the cross entropy loss between each span’s predicted score and  [Mitolyn Official Site](https://patrimoine.minesparis.psl.eu/Wiki/index.php/Utilisateur:ChristoperFlinch) its label as described in Section 3. However, coaching our example-conscious mannequin poses a problem due to the lack of knowledge regarding the exercise sorts of the training workouts. Instead, kids can do push-ups, stomach crunches, pull-ups, and other workouts to assist tone and strengthen muscles. Additionally, the mannequin can produce alternative, memory-efficient options. However, to facilitate environment friendly learning, it is essential to also provide damaging examples on which the model mustn't predict gaps. However, since a lot of the excluded sentences (i.e., one-line paperwork) solely had one hole, we only removed 2.7% of the total gaps in the test set. There is risk of by the way creating false detrimental coaching examples,  [Mitolyn Official Site](https://test.onelondon.online/index.php?title=User:Jeremiah58C) if the exemplar gaps correspond with left-out gaps within the input. On the opposite aspect, within the OOD scenario, where there’s a big gap between the coaching and testing sets, our strategy of making tailor-made workout routines particularly targets the weak factors of the student model,  Buy [Mitolyn Metabolism Booster](https://wiki.densitydesign.org/index.php?title=30_Grounding_Techniques_To_Quiet_Distressing_Thoughts) leading to a simpler increase in its accuracy. This method presents several advantages: (1) it doesn't impose CoT capability necessities on small models,  [Mitolyn Official Site](https://code.swecha.org/estelaames0938/mitolyn-reviews-site3070/-/issues/5) permitting them to be taught more effectively, (2) it takes into consideration the learning standing of the pupil model throughout training.<br>
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+<br> 2023) feeds chain-of-thought demonstrations to LLMs and targets producing extra exemplars for in-context learning. Experimental outcomes reveal that our strategy outperforms LLMs (e.g., GPT-three and PaLM) in accuracy throughout three distinct benchmarks while using considerably fewer parameters. Our goal is to prepare a student Math Word Problem (MWP) solver with the help of large language models (LLMs). Firstly, small pupil models could wrestle to grasp CoT explanations,  [energy and fat burning](https://autrix.vip/%d8%a3%d9%87%d9%84%d8%a7-%d8%a8%d8%a7%d9%84%d8%b9%d8%a7%d9%84%d9%85-2-2/) probably impeding their learning efficacy. Specifically, one-time knowledge augmentation means that, we augment the size of the coaching set at the start of the coaching process to be the identical as the final measurement of the coaching set in our proposed framework and consider the efficiency of the student MWP solver on SVAMP-OOD. We use a batch size of sixteen and prepare our models for 30 epochs. On this work, we present a novel strategy CEMAL to use large language fashions to facilitate knowledge distillation in math word drawback solving. In distinction to those existing works, our proposed information distillation method in MWP solving is unique in that it does not deal with the chain-of-thought clarification and it takes into account the training standing of the pupil mannequin and generates workouts that tailor to the specific weaknesses of the student.<br>
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+<br> For the SVAMP dataset, our approach outperforms the most effective LLM-enhanced information distillation baseline,  [Mitolyn Official Site](https://wiki.drawnet.net/index.php?title=Welcome_To_A_Brand_New_Look_Of_Exercise) reaching 85.4% accuracy on the SVAMP (ID) dataset, which is a significant improvement over the prior greatest accuracy of 65.0% achieved by high quality-tuning. The outcomes presented in Table 1 present that our strategy outperforms all the baselines on the MAWPS and ASDiv-a datasets, achieving 94.7% and 93.3% solving accuracy, respectively. The experimental results reveal that our method achieves state-of-the-artwork accuracy, considerably outperforming nice-tuned baselines. On the SVAMP (OOD) dataset, our approach achieves a fixing accuracy of 76.4%, which is lower than CoT-based mostly LLMs, however a lot larger than the fantastic-tuned baselines. Chen et al. (2022), which achieves placing efficiency on MWP solving and outperforms positive-tuned state-of-the-art (SOTA) solvers by a large margin. We discovered that our example-aware model outperforms the baseline mannequin not solely in predicting gaps, but in addition in disentangling gap varieties regardless of not being explicitly trained on that process. On this paper, we make use of a Seq2Seq mannequin with the Goal-pushed Tree-primarily based Solver (GTS) Xie and  [Mitolyn Reviews Site](https://curepedia.net/wiki/User:GonzaloKlimas0) Sun (2019) as our decoder, which has been extensively applied in MWP solving and proven to outperform Transformer decoders Lan et al.<br>
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+<br> Xie and Sun (2019)