Encoder and Decoder definition

Encoder and Decoder means On2's VP5 and VP6 encoder and decoder algorithm. Such encoders being "Encoders" and such decoders being "Decoders."
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Encoder and Decoder means On2's VP5 and VP6 encoder and decoder algorithm. Such encoders being "Encoders" and such decoders being "Decoders." 2 <PAGE> 1.16 "Initial Port" means the porting of the Encoder and Decoder to the Chips by E-world. E-world agrees to start the Initial Port within a week of receiving the Source Code and to use reasonable best efforts to complete the Initial Port as quickly as possible. On2 agrees to use reasonable best efforts pursuant to Section 2.11 hereof to provide technical support relating to the Initial Port. 1.17 "Chips" means the two alternate digital signal processing chips selected by E-world and On2 jointly for the purpose of running the Encoder and Decoder. The digital signal processing chips selected will have sufficient performance characteristics to support the resolutions and framerates selected for the EVD system and non-EVD system. 1.18 "Update" means changes or additions to the Source Code made commercially available by On2, including, without limitation, revisions, patches and bug fixes and maintenance releases. 2
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Examples of Encoder and Decoder in a sentence

  • To verify the Truth-tables of all logic gates.2. To realize and verify the Half & full adder circuits using logic gates.3. To realize Half & full subtractor circuits using logic gates.4. To realize Encoder and Decoder circuits5.

  • You can set its MetadataExtractor if you need to switch to a different mime type or register additional metadata mime types.You’ll need to set the Encoder and Decoder instances required for metadata and data formats to support.

  • The spring-web module contains Encoder and Decoder implementations such as Jackson CBOR/JSON, and Protobuf that RSocket applications will likely need.

  • Ϭ͘ϳϱ Φ (Seventy-Five Euro cents) Licensed Consumer Product with both Encoder and Decoder functions.

  • Neural Architecture‌The next two process steps, Encoder and Decoder, are what we call the Neural Architecture.

  • In Rank-AE, we use the fixed neural network architecture, with two fully con- nected layers in both Encoder and Decoder, and one fully connected layer following Embedding & Atten network in Feature Embedding.

  • Longformer Encoder and Decoder (LED) [1] and PRIMERA [58] are two models that can handle longer inputs (16384 and 4096 tokens, respectively) by introducing sparsity into attention layers, and PRIMERA adds an MDS-specific pre-training objective to improve performance on MDS tasks.

  • The Embedding Tables, Encoder and Decoder imple- mentations were inherited from the HuggingFace- transformers library 7 and spaCy was employed to produce word-dependency features.

  • It is a deep learning model which has two parts like the other models: Encoder and Decoder [16].

  • Direct chaining of the Encoder and Decoder is effectively an autoencoder [17].

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