**Assemble-ResNet** is a modification to the [ResNet](https://paperswithcode.com/method/resnet) architecture with several tweaks including using [ResNet-D](https://paperswithcode.com/method/resnet-d), channel attention, [anti-alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), and Big Little Networks.

**Partition Filter Network** is a framework designed specifically for joint entity and relation extraction. The framework consists of three components: partition filter encoder, NER unit and RE unit. In task units, we use table-filling for word pair prediction. Orange, yellow and green represents NER-related, shared and RE-related component or features. (b) Detailed depiction of partition filter encoder in one single time step. We decompose feature encoding into two steps: partition and filter (shown in the gray area). In partition, we first segment neurons into two task partitions and one shared partition. Then in filter, partitions are selected and combined to form task-specific features and shared features, filtering out information irrelevant to each task.

Partition Filter Network

A Partition Filter Network for Joint Entity and Relation Extraction

Assemble-ResNet

Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network

The **Recurrent Entity Network** is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. For language understanding tasks, it can reason on-the-fly as it reads text, not just when it is required to answer a question or respond as is the case for a [Memory Network](https://paperswithcode.com/method/memory-network). Like a [Neural Turing Machine](https://paperswithcode.com/method/neural-turing-machine) or Differentiable Neural Computer, it maintains a fixed size memory and can learn to perform location and content-based read and write operations.  However, unlike those models it has a simple parallel  architecture in which several memory locations can be updated simultaneously. 

The model consists of a fixed number of dynamic memory cells, each containing a vector key $w_j$ and a vector value (or content) $h_j$. Each cell is associated with its own processor, a simple gated recurrent network that may update the cell value given an input. If each cell learns to represent a concept or entity in the world, one can imagine a gating mechanism that, based on the key and content of the memory cells, will only modify the cells that concern the entities mentioned in the input. There is no direct interaction between the memory cells, hence the system can be seen as multiple identical processors functioning in parallel, with distributed local memory. 

The sharing of these parameters reflects an invariance of these laws across object instances, similarly to how the [weight tying](https://paperswithcode.com/method/weight-tying) scheme in a CNN reflects an invariance of image statistics across locations. Their hidden state is updated only when new information relevant to their concept is received, and remains otherwise unchanged. The keys used in the addressing/gating mechanism also correspond to concepts or entities, but are modified only during learning, not during inference.

Source	Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
Year	2000
Data Source	Papers with Code archive (CC BY-SA)