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https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#46 | here the softmax is applied over each of the rows of the matrix.
The number of dimensions in a query vector is query size and similarly for the key size and value size . The output dimension of an attention head is its head dimension . The attention mechanism requires the following three equalities to hold:but is other... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#47 | ention fashion, then usually . It is theoretically possible for all three to be different, but that is rarely the case in practice.
Multiheaded attention
[edit]One set of matrices is called an attention head, and each layer in a transformer model has multiple attention heads. While each attention head attends to the to... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#48 | . Specifically, the query and key projection matrices, and , which are involved in the attention score computation, defines the "relevance". Meanwhile, the value projection matrix , in combination with the part of the output projection matrix , determines how the attended tokens influence what information is passed to ... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#49 | red by each attention head, can expand as tokens pass through successive layers. This allows the model to capture more complex and long-range dependencies in deeper layers. Many transformer attention heads encode relevance relations that are meaningful to humans. For example, some attention heads can attend mostly to t... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#50 | erformed in parallel, which allows for fast processing. The outputs for the attention layer are concatenated to pass into the feed-forward neural network layers.
Concretely, let the multiple attention heads be indexed by , then we have where the matrix is the concatenation of word embeddings, and the matrices are "proj... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#51 | ead.
It is theoretically possible for each attention head to have a different head dimension , but that is rarely the case in practice.
As an example, in the smallest GPT-2 model, there are only self-attention mechanisms. It has the following dimensions:Since , its output projection matrix is a square matrix.
Masked at... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#52 | on of the first output token , for step , the output token shall remain constant. This ensures properties of the model similar to autoregressive models.[1] Therefore, at every time step , the calculation for all outputs should not have access to tokens at position for (as it naturally is the case for time step , when t... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#53 | es where the attention link must be cut, and at other places: The following matrix is commonly used in decoder self-attention modules, called "causal masking":
In words, it means that each token can pay attention to itself, and every token before it, but not any after it. A non-masked attention module can be thought of... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#54 | iders all masks of the form , where is a random permutation matrix.[57]
Encoder
[edit]An encoder consists of an embedding layer, followed by multiple encoder layers.
Each encoder layer consists of two major components: a self-attention mechanism and a feed-forward layer. It takes an input as a sequence of input vectors... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#55 | ch vector individually. Schematically, we have:
where stands for "feed-forward network". We can more succinctly write it aswith the implicit convention that the is applied to each row of the matrix individually.
The encoder layers are stacked. The first encoder layer takes the sequence of input vectors from the embeddi... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#56 | the final encoder layer is then used by the decoder.
As the encoder processes the entire input all at once, every token can attend to every other token (all-to-all attention), so there is no need for causal masking.
Decoder
[edit]A decoder consists of an embedding layer, followed by multiple decoder layers, followed by... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#57 | ntion mechanism, and a feed-forward neural network. The decoder functions in a similar fashion to the encoder, but an additional attention mechanism is inserted which instead draws relevant information from the encodings generated by the encoders. This mechanism can also be called the encoder-decoder attention.[1][54]
... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#58 | than encodings. The transformer must not use the current or future output to predict an output, so the output sequence must be partially masked to prevent this reverse information flow.[1] This allows for autoregressive text generation. For decoding, all-to-all attention is inappropriate, because a token cannot attend ... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#59 | echanism attends to the output vectors of the encoder, which is computed before the decoder starts decoding. Consequently, there is no need for masking in the cross-attention mechanism.
Schematically, we have:where is the matrix with rows being the output vectors from the encoder.
The last decoder is followed by a fina... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#60 | probability, and the decoder can be run again to produce the next token, etc, autoregressively generating output text.
Adapted architectures
[edit]Many large language models, since they do not need to predict a whole new sequence from an input sequence, only use the encoder or decoder of the original transformer archit... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#61 | l, only makes use of an encoder, and is trained to predict a randomly masked token in a sequence.[35]
Full transformer architecture
[edit]Sublayers
[edit]Each encoder layer contains 2 sublayers: the self-attention and the feedforward network. Each decoder layer contains 3 sublayers: the causally masked self-attention, ... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#62 | (LayerNorm, or LN), which while conceptually unnecessary, are necessary for numerical stability and convergence.
The residual connection, which is introduced to avoid vanishing gradient issues and stabilize the training process, can be expressed as follows: y = F(x) + x. The expression indicates that an output y is the... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#63 | void issues when the gradient of F(x) is close to zero.
Similarly to how the feedforward network modules are applied individually to each vector, the LayerNorm is also applied individually to each vector.
There are two common conventions in use: the post-LN and the pre-LN convention. In the post-LN convention, the outp... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#64 | ayer isThe original 2017 Transformer used the post-LN convention. It was difficult to train and required careful hyperparameter tuning and a "warm-up" in learning rate, where it starts small and gradually increases. The pre-LN convention, proposed several times in 2018,[59] was found to be easier to train, requiring no... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#65 | r Transformer, adapted from[60]
input: Encoder input t_e Decoder input t_d output: Array of probability distributions, with shape (decoder vocabulary size x length(decoder output sequence)) /* encoder */ z_e ← encoder.tokenizer(t_e) for each t in 1:length(z_e) do z_e[t] ← encoder.embedding(z_e[t]) + encoder.positional_... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#66 | h t in 1:length(z_e) do z_e[t] ← layer.layer_norm(z_e[t]) z_e ← layer.multiheaded_attention(z_e, z_e, z_e) for each t in 1:length(z_e) do z_e[t] ← z_e[t] + z_e_copy[t] /* second sublayer */ z_e_copy ← copy(z_e) for each t in 1:length(z_e) do z_e[t] ← layer.layer_norm(z_e[t]) z_e ← layer.feedforward(z_e) for each t in 1... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#67 | */ z_d ← decoder.tokenizer(t_d) for each t in 1:length(z_d) do z_d[t] ← decoder.embedding(z_d[t]) + decoder.positional_embedding(t) for each l in 1:length(decoder.layers) do layer ← decoder.layers[l] /* first sublayer */ z_d_copy ← copy(z_d) for each t in 1:length(z_d) do z_d[t] ← layer.layer_norm(z_d[t]) z_d ← layer.m... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#68 | copy ← copy(z_d) for each t in 1:length(z_d) do z_d[t] ← layer.layer_norm(z_d[t]) z_d ← layer.multiheaded_attention(z_d, z_e, z_e) for each i in 1:length(z_d) do z_d[t] ← z_d[t] + z_d_copy[t] /* third sublayer */ z_d_copy ← copy(z_d) for each t in 1:length(z_d) do z_d[t] ← layer.layer_norm(z_d[t]) z_d ← layer.feedforwa... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#69 | for each t in 1:length(z_d) do output_distributions.append(decoder.unembed(z_d[t])) return output_distributions
Terminology
[edit]The Transformer architecture, being modular, allows variations. Several common variations are described here.[61]
An "encoder-only" Transformer applies the encoder to map an input text into ... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#70 | stream applications. BERT is encoder-only. They are less often used currently, as they were found to be not significantly better than training an encoder-decoder Transformer, then taking just the encoder.[51]
A "decoder-only" Transformer is not literally decoder-only, since without an encoder, the cross-attention mecha... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#71 | lly masked self-attention, and the feedforward network. This is usually used for text generation and instruction following. The models in the GPT series and Chinchilla series are decoder-only.
An "encoder-decoder" Transformer is generally the same as the original Transformer, with 2 sublayers per encoder layer and 3 su... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#72 | ng the location of normalization, etc. This is also usually used for text generation and instruction following. The models in the T5 series are encoder-decoder.[61]
A "prefixLM" (prefix language model) is a decoder-only architecture, but with prefix masking, which is different from causal masking. Specifically, it has ... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#73 | toregressively generated text based on the prefix. They resemble encoder-decoder models, but has less "sparsity". Such models are rarely used, though they are cited as theoretical possibilities and benchmarked comparisons.[51]
There are also mixed seq2seq models. For example, in 2020, Google Translate replaced the prev... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#74 | r than Transformer-decoder when run autoregressively.[62]
Subsequent work
[edit]Alternative activation functions
[edit]The original transformer uses ReLU activation function. Other activation functions were developed. The Llama series and PaLM used SwiGLU;[63] both GPT-1 and BERT[35] used GELU.[64]
Alternative activati... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#75 | ]The normalization used in the Transformer can be different from LayerNorm. One example is RMSNorm[65] which is used in the Llama series. Other examples include CapsuleNorm[66] ScaleNorm,[67] or FixNorm.[67]
Alternative positional encodings
[edit]Transformers may use other positional encoding methods than sinusoidal.[6... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#76 | one.[1] Later,[70] found that causal masking itself provides enough signal to a Transformer decoder that it can learn to implicitly perform absolute positional encoding without the positional encoding module.
RoPE
[edit]RoPE (rotary positional embedding),[71] is best explained by considering a list of 2-dimensional vec... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#77 | PE encoding is just multiplication by an angle:For a list of -dimensional vectors, a RoPE encoder is defined by a sequence of angles . Then the RoPE encoding is applied to each pair of coordinates.
The benefit of RoPE is that the dot-product between two vectors depends on their relative location only: for any integer .... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#78 | Instead, it is an additional positional encoder that is directly plugged into the attention mechanism. Specifically, the ALiBi attention mechanism isHere, is a real number ("scalar"), and is the linear bias matrix defined byin other words, . The idea being that the linear bias matrix is a softened mask. Just as represe... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#79 | creases attention paid in the other direction.
ALiBi allows pretraining on short context windows, then fine-tuning on longer context windows. Since it is directly plugged into the attention mechanism, it can be combined with any positional encoder that is plugged into the "bottom" of the entire network (which is where ... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#80 | it]Relative Position Encodings[73] is similar to ALiBi, but more generic:where is a Toeplitz matrix, that is, whenever . This is contrasted with the original sinusoidal positional encoding, which is an "absolute positional encoding".[74]
Efficient implementation
[edit]The transformer model has been implemented in stand... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#81 | sformer-based architectures and pretrained models.[11]
KV caching
[edit]When an autoregressive transformer is used for inference, such as generating text, the query vector is different at each step, but the already-computed key and value vectors are always the same. The KV caching method saves the computed key and valu... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#82 | aching.[75][76][77]
If a transformer is used with a baked-in prompt, such as ["You are a customer support agent..."], then the key and value vectors can be computed for the prompt, and saved on disk. The saving in compute is significant when the model is used for many short interactions, such as in online chatbots.
Fla... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#83 | is a communication-avoiding algorithm that performs matrix multiplications in blocks, such that each block fits within the cache of a GPU, and by careful management of the blocks it minimizes data copying between GPU caches (as data movement is slow). See the page on softmax for details.
An improved version, FlashAtten... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#84 | offers enhancements in work partitioning and parallelism, enabling it to achieve up to 230 TFLOPs/s on A100 GPUs (FP16/BF16), a 2x speed increase over the original FlashAttention.
Key advancements in FlashAttention-2 include the reduction of non-matmul FLOPs, improved parallelism over the sequence length dimension, bet... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#85 | ped-query attention (GQA).[82]
Benchmarks revealed FlashAttention-2 to be up to 2x faster than FlashAttention and up to 9x faster than a standard attention implementation in PyTorch. Future developments include optimization for new hardware like H100 GPUs and new data types like FP8.
Multi-Query Attention
[edit]
Multi-... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#86 | thus:
This has a neutral effect on model quality and training speed, but increases inference speed.
More generally, grouped-query attention (GQA) partitions attention heads into groups, each of which shares the key-value pair. MQA is GQA with one group, while standard multiheaded attention is GQA with the maximal numbe... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#87 | before entering the attention mechanism, is first projected to two low-dimensional spaces ("latent space"), one for query and one for key-value (KV vector). This design minimizes the KV cache, as only the low-dimensional KV vector needs to be cached.[85]
Speculative decoding
[edit]Speculative decoding[86][87] is a meth... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#88 | the quickly computed tokens are incorrect, they are discarded and computed slowly.
The key factor in speculative decoding is that a Transformer decoder can verify faster than it can decode, in the following sense.
Suppose we have two transformer models like GPT-3 and GPT-3-small, both with a context window size of 512.... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#89 | enerating a token , taking time . However, if we had some educated guess for the values of these tokens, we could verify all of them in parallel, in one run of the model, by checking that each is indeed the token with the largest log-likelihood in the -th output.
In speculative decoding, a smaller model or some other s... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#90 | ose we use GPT-3-small to generate four speculative tokens: . This only takes . These tokens are then run through the larger GPT-3 in one go. Suppose that and are verified by GPT-3 as what it would have picked, then those are kept, but is not, so are discarded, and GPT-3 is run on those. This would take , which might b... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#91 | ly, in a way that guarantees the final output distribution is the same as if speculative decoding was not used.[86][88]
In Multi-Token Prediction, a single forward pass creates a final embedding vector, which then is un-embedded into a token probability. However, that vector can then be further processed by another Tra... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#92 | , since each new token costs just one more Transformer block, rather than the entire stack.[89][90]
Sub-quadratic transformers
[edit]Training transformer-based architectures can be expensive, especially for long inputs.[91] Many methods have been developed to attempt to address the issue. In the image domain, Swin Tran... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#93 | attention in time and frequency domains.[93] Long Range Arena (2020)[94] is a standard benchmark for comparing the behavior of transformer architectures over long inputs.
Alternative attention graphs
[edit]The standard attention graph is either all-to-all or causal, both of which scales as where is the number of tokens... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#94 | layers.[96]
Sparse attention[97] uses attention graphs that grows slower than . For example, BigBird (2020)[98] uses random small-world networks which grows as .
Ordinary transformers require a memory size that is quadratic in the size of the context window. Attention-free transformers[99] reduce this to a linear depen... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#95 | m Feature Attention (2021)[100] uses Fourier random features:where are independent samples from the normal distribution . This choice of parameters satisfy , or Consequently, the one-headed attention, with one query, can be written as where . Similarly for multiple queries, and for multiheaded attention.
This approxima... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#96 | ged to obtain a more precise version of Performer (2022)[101] uses the same Random Feature Attention, but are first independently sampled from the normal distribution , then they are Gram-Schmidt processed.
Multimodality
[edit]Transformers can also be used/adapted for modalities (input or output) beyond just text, usua... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#97 | udy found that Transformers pretrained only on natural language can be finetuned on only 0.03% of parameters and become competitive with LSTMs on a variety of logical and visual tasks, demonstrating transfer learning.[102] The LLaVA was a vision-language model composed of a language model (Vicuna-13B)[103] and a vision... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#98 | rmer to computer vision by breaking down input images as a series of patches, turning them into vectors, and treating them like tokens in a standard transformer.
Conformer[42] and later Whisper[105] follow the same pattern for speech recognition, first turning the speech signal into a spectrogram, which is then treated... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#99 | Perceivers[106][107] are a variant of Transformers designed for multimodality.
For image generation, notable architectures are DALL-E 1 (2021), Parti (2022),[108] Phenaki (2023),[109] and Muse (2023).[110] Unlike later models, DALL-E is not a diffusion model. Instead, it uses a decoder-only Transformer that autoregress... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#100 | an image.[111] Parti is an encoder-decoder Transformer, where the encoder processes a text prompt, and the decoder generates a token representation of an image.[112] Muse is an encoder-only Transformer that is trained to predict masked image tokens from unmasked image tokens. During generation, all input tokens are mas... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#101 | text-to-video model. It is a bidirectional masked transformer conditioned on pre-computed text tokens. The generated tokens are then decoded to a video.[109]
Applications
[edit]The transformer has had great success in natural language processing (NLP). Many large language models such as GPT-2, GPT-3, GPT-4, Gemini, Alb... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#102 | related subtasks and their related real-world applications, including:
- machine translation
- time series prediction
- document summarization
- document generation
- named entity recognition (NER)[113]
- writing computer code based on requirements expressed in natural language.
- speech-to-text
Beyond traditional NLP,... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#103 | - protein folding (such as AlphaFold)
- evaluating chess board positions. Using static evaluation alone (that is, with no Minimax search) transformer achieved an Elo of 2895, putting it at grandmaster level.[10]
See also
[edit]- seq2seq – Family of machine learning approaches
- Perceiver – Variant of Transformer design... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#104 | ne learning model
- BERT (language model) – Series of language models developed by Google AI
- Generative pre-trained transformer – Type of large language model
- T5 (language model) – Series of large language models developed by Google AI
Notes
[edit]- ^ Gated recurrent units (2014) further reduced its complexity.
- ^... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#105 | hish; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan N; Kaiser, Łukasz; Polosukhin, Illia (2017). "Attention is All you Need" (PDF). Advances in Neural Information Processing Systems. 30. Curran Associates, Inc.
- ^ Hochreiter, Sepp; Schmidhuber, Jürgen (1 November 1997). "Long Short-Term Mem... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#106 | Better Language Models and Their Implications". OpenAI. 2019-02-14. Archived from the original on 2020-12-19. Retrieved 2019-08-25.
- ^ a b Bahdanau; Cho, Kyunghyun; Bengio, Yoshua (September 1, 2014). "Neural Machine Translation by Jointly Learning to Align and Translate". arXiv:1409.0473 [cs.CL].
- ^ Luong, Minh-Than... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#107 | Xiv:1508.04025 [cs.CL].
- ^ a b Chen, Lili; Lu, Kevin; Rajeswaran, Aravind; Lee, Kimin; Grover, Aditya; Laskin, Michael; Abbeel, Pieter; Srinivas, Aravind; Mordatch, Igor (2021-06-24), Decision Transformer: Reinforcement Learning via Sequence Modeling, arXiv:2106.01345
- ^ Parisotto, Emilio; Song, Francis; Rae, Jack; P... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#108 | , Matthew; Heess, Nicolas; Hadsell, Raia (2020-11-21). "Stabilizing Transformers for Reinforcement Learning". Proceedings of the 37th International Conference on Machine Learning. PMLR: 7487–7498.
- ^ Radford, Alec; Jong Wook Kim; Xu, Tao; Brockman, Greg; McLeavey, Christine; Sutskever, Ilya (2022). "Robust Speech Reco... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#109 | ruary 2023). "Learning to Throw With a Handful of Samples Using Decision Transformers". IEEE Robotics and Automation Letters. 8 (2): 576–583. doi:10.1109/LRA.2022.3229266. ISSN 2377-3766.
- ^ a b Ruoss, Anian; Delétang, Grégoire; Medapati, Sourabh; Grau-Moya, Jordi; Wenliang, Li; Catt, Elliot; Reid, John; Genewein, Tim... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#110 | Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Pierric; Rault, Tim; Louf, Remi; Funtowicz, Morgan; Davison, Joe; Shleifer, Sam; von Platen, Patrick; Ma, Clara; Jernite, Yacine; Plu, Julien; Xu, Canwen; Le Scao, Teven; Gugger, Sylvain; Drame, Mariama; Lhoest, Quentin; Rush, Alexander (2020). "Transfo... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#111 | Processing: System Demonstrations. pp. 38–45. doi:10.18653/v1/2020.emnlp-demos.6. S2CID 208117506.
- ^ a b c "Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing". Google AI Blog. 2 November 2018. Archived from the original on 2021-01-13. Retrieved 2019-08-25.
- ^ Feldman, J. A.; Ballard, ... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#112 | 1-3. ISSN 0364-0213.
- ^ Rumelhart, David E.; McClelland, James L.; Hinton, Geoffrey E. (1987-07-29). Parallel Distributed Processing, Volume 1: Explorations in the Microstructure of Cognition: Foundations, Chapter 2 (PDF). Cambridge, Mass: Bradford Books. ISBN 978-0-262-68053-0.
- ^ Giles, C. Lee; Maxwell, Tom (1987-1... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#113 | O.26.004972. ISSN 0003-6935. PMID 20523475.
- ^ a b Schmidhuber, Jürgen (1992). "Learning to control fast-weight memories: an alternative to recurrent nets" (PDF). Neural Computation. 4 (1): 131–139. doi:10.1162/neco.1992.4.1.131. S2CID 16683347.
- ^ Christoph von der Malsburg: The correlation theory of brain function.... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#114 | eural Networks II, chapter 2, pages 95–119. Springer, Berlin, 1994.
- ^ Jerome A. Feldman, "Dynamic connections in neural networks," Biological Cybernetics, vol. 46, no. 1, pp. 27–39, Dec. 1982.
- ^ Hinton, Geoffrey E.; Plaut, David C. (1987). "Using Fast Weights to Deblur Old Memories". Proceedings of the Annual Meeti... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#115 | nsformers are RNNs: Fast autoregressive Transformers with linear attention". ICML 2020. PMLR. pp. 5156–5165.
- ^ Schlag, Imanol; Irie, Kazuki; Schmidhuber, Jürgen (2021). "Linear Transformers Are Secretly Fast Weight Programmers". ICML 2021. Springer. pp. 9355–9366.
- ^ a b c Cho, Kyunghyun; van Merriënboer, Bart; Gulc... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#116 | using RNN Encoder–Decoder for Statistical Machine Translation". In Moschitti, Alessandro; Pang, Bo; Daelemans, Walter (eds.). Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar: Association for Computational Linguistics. pp. 1724–1734. arXiv:1406.1078. doi:10.311... |
https://en.wikipedia.org/wiki/Transformer_%28deep_learning_architecture%29#117 | networks". arXiv:1409.3215 [cs.CL]. [first version posted to arXiv on 10 Sep 2014]
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