当前流行的sparse检索,大概是通过transformer模型,为doc中的term计算weight,这样与传统的BM25等基于频率的方法相比,sparse向量可以利用神经网络的力量,提高了检索的准确性和效率。BM25虽然能够计算文档的相关性,但它无法理解词语的含义或上下文的重要性。而稀疏向量则能够通过神经网络捕捉到这些细微的差别。 稀疏向量的优势 ...
The neural processing unit is reconfigurable to process a fine-grained structure sparsity weight array selected from N:M = 1:4, 2:4, 2:8, and 4:8 fine-grained structure weight sparsity arrays. The weight buffer stores weight values, and the weight multiplexer array outputs one or more ...
为了提高模型对相机内外参泛化性,我们在Sparse4D v2中加入了内外参的编码,将相机投影矩阵通过全连接网络映射到高维特征空间得到camera embed。在计算deformable aggregation中的attention weightsW时,我们不仅考虑instance feature和anchor embed,还加上了camera embed。 W=\textbf{FC}(I+E+E_{cam}) 在实验中,我们发现...
8-bit weight and activation quantization support. efficient usage of cached attention keys and values for minimal memory movement. Try It Now Install (requires Linux): pip install -U deepsparse-nightly[llm] Run inference: from deepsparse import TextGeneration pipeline = TextGeneration(model="zoo:...
A weighted average of memory vectors is returned, where those memory vectors more similar to the query vector are given more weight. A simple SQHN network with a single hidden layer can be interpreted as performing a kind of nearest neighbor operation as well, where β = ∞: $${x}_...
expert weight生成网络遵循动态卷积结构的基本设计,如图3(b)所示还使用了softmax中的temperature annealing operation (tao)来控制expert weight,使训练过程更加有效。 作者还构造了一个Staircase Structure来聚集来自不同金字塔层的特征。P2 到P5 的特征在尺度上是依次下降的:Pi 的宽和高是Pi−1 的1/2。 最后,将...
而对比dense head,如CenterPoint或BEV3D,其分类label为heatmap,随着离GT距离增大,loss weight会发生变化。 因此,除了一个正负样本的分类置信度以外,还需要一个描述模型结果与GT匹配程度的置信度,也就是进行Quality Estimation。对于3D检测来说,我们定义了两个quality指标,centerness和yawness,公式如下: 对于centerness...
A light weight MATLAB library for making exsiting images to videos:img2vid An adaptive filter to remove isolate hot pixels:Adaptive filter imagej-plugin A tool for multi-color 2D or 3D imaging:Merge channels Further reading:#behind_the_paper&blog ...
weight, indice_pairs.to(device), indice_pair_num, outids.shape[0], self.algo) else: # 执行卷积(SparseConv3d) (64000, 16) --> (109815, 16) out_features = Fsp.indice_conv(features, self.weight, indice_pairs.to(device), indice_pair_num, outids.shape[0], self.algo) if self.bias...
.github/workflows examples sinkhorn_transformer .gitignore LICENSE README.md divine.png setup.py sinkhorn.png sortcut.png README MIT license Sinkhorn Transformer This is a reproduction of the work outlined inSparse Sinkhorn Attention, with additional enhancements. ...