In this paper, we propose a new sparse matrix format in order to enable a highly parallel decoding process of the entire sparse matrix. The proposed sparse matrix is constructed by combining pruning and weight quantization. For the latest RNN models on PTB and WikiText-2 corpus, LSTM ...
based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the ...
Although it is beyond the scope of this paper, the virtual neu- ron is a vital component for composing subnetworks to scale-up neuromorphic algorithms, and it is also a vital component for network encoding and decoding capabilities. We would like to address these areas in future work. The ...
Attention mechanism is first proposed to pay attention to different parts of the source sentence at every decoding step in neural machine translation [16]. In seq2seq learning, we use a guider to distinguish the importance of tokens. Suppose we have a sentence, which has n tokens, represented...
In contrast with V1 recordings, decoding of the irrelevant modality gradually declined in ACC after an initial transient. Our analytical proof and a recurrent neural network model of the task revealed mutually inhibiting connections that produced context-gated suppression as observed in mice. Using ...
The loss function used is cross-entropy loss, and the model is validated using BLEU-4 during training. During inference, since the length of the predicted sentence is unknown, the relative position information is removed for the RDNpos model. A beam size of 3 is used in the decoding ...
Figure 2. General architecture of the model and the three position encoding methods adopted in each version of the model. The image is first input into ResNet to extract features, then combined with the position encoding, and then input into Transformer for decoding to obtain the description sen...
Attention-based networks emphasize the most relevant parts of the source sequence during each decoding time step. In doing so, the encoder sequence is treated as a soft-addressable memory whose positions are weighted based on the state of the decoder RNN. Bidirectional RNNs learn past and future...
For decoding, the network generates a candidate sentence as the following sentence of the current sentence. We use cosine similarity as a scoring function to assign scores to the candidate embedding and the embedding of other sentences in the shuffled set. Then a Brute Force Search is employed ...
At the same time, the lightweight network structure of HGNetv2 from RT-DETR [21] is introduced to replace the backbone network of YOLOv8-Pose, reducing the model complexity and improving the inference speed, thereby effectively improving the decoding efficiency. In addition, several modules in ...