Low-Rank RNN Adaptation for Context-Aware Language Modelingdoi:10.1162/TACL_A_00035Aaron JaechMari OstendorfMIT Press One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@mit.edu
在现有的代表性序列推荐方法中,由于消失梯度问题,基于RNN的方法不足以对长序列进行建模[33]。结合位置信息的基于注意力的方法需要明确定义位置嵌入函数[47]或注入可学习的位置嵌入[46]。与基于RNN和基于注意力的方法相比,张量投影方法可以有效地处理长序列表示并描述序列,而无需明确定义或学习位置嵌入[22]。因此,低秩...
Extracting computational mechanisms from neural data using low-rank RNNs. In Advances in Neural Information Processing Systems (eds. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A.) 24072–24086 (Curran Associates, Inc., 2022). Holland, J. H. Hidden Order: ...
在IID和non-IID的情况下都进行了实验。 所用的模型,对于卷积层的实验,选用VGG和ResNet;对于RNN的结构选用了LSTM;对于全链接层则选择了两层全链接的MLP模型。 对于超参数的设定,则是使用了一些约束的手段r= (1-\gamma)r_{min}+\gamma r_{max}其中r_{min}是让近似矩阵达到满秩的最小秩,r_{max}则是限...
Paper tables with annotated results for Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
(BN) caching approach to eliminate the redundant computations during multiple sweeps of development data. Experimental results on the short message dictation (SMD) task show that the eLRPD adaptation can reduce the SD footprints by 82\% for the SVD DNN and 96\% for the LSTM-...
Paper tables with annotated results for Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation
rnn(x) 72 + h = self.dropout(final_states[0].squeeze()) 73 + y_1 = self.linear_1(h) 74 + return y_1 75 + 76 + 77 + class LMF(nn.Module): 78 + ''' 79 + Low-rank Multimodal Fusion 80 + ''' 81 + 82 + def __init__(self, input_dims,...
1、Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing Jonas Mueller MIT CSAIL Vasilis Syrgkanis Microsoft Research vasy Matt Taddy Chicago Booth Abstract We consider dynamic pricing with many products under an evolving but low- dimensional deman 2、d model. Assuming the temporal variation ...
rnn You can install the dependencies: luarocks install rnn Training Please follow the instruction fromVQA_LSTM_CNNfor preprocessing.--split 2option allows to use train+val set to train, and test-dev or test-standard set to evaluate. Set--num_ansto2000to reproduce the result. ...