Summarizing https://arxiv.org/pdf/2302.10866.pdf
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The paper proposes Hyena, a new architecture for language models that uses hierarchical convolutional layers to process input sequences of variable lengths. The authors show that Hyena outperforms previous state-of-the-art models on several benchmarks and achieves competitive results on the WikiText-103 dataset. They also demonstrate that Hyena can be trained with significantly fewer parameters while still achieving strong performance. Additionally, they introduce the Hyena hierarchy, an operator defined by a recurrence of two ecient subquadratic primitives: a long convolution and element-wise multiplicative gating (see Figure 1.1). A specified depth (i.e., number of steps) of the recurrence controls the size of the operator. By mapping each step in the Hyena recurrence to its corresponding matrix form, they reveal Hyena operators to be equivalently defined as a decomposition of a data-controlled matrix i.e., a matrix whose entries are functions of the input. Furthermore, they show how Hyena operators can be evaluated efficiently without materializing the full matrix, by leveraging fast convolution algorithms (Selesnick and Burr
The authors also introduce a new benchmark for evaluating language models on text summarization tasks, which is based on the ROUGE metric and includes a diverse set of languages and domains. They demonstrate that Hyena outperforms previous state-of-the-art models on this benchmark, achieving competitive results even with fewer parameters. Finally, they provide an analysis of the learned representations in Hyena, showing that it captures both local and global contextual information in the input sequence.
Summarizing https://arxiv.org/pdf/2212.14052.pdf
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We introduce a new approach to language modeling using state space models (SSMs) that incorporate sequential dependencies between words in a sentence and capture the dependencies between words in a sentence. Our SSM outperforms traditional language models such as Transformer and LSTM-based models on the Hungry Hungry Hippos dataset, which consists of short sentences with missing words. Additionally, we propose a new SSM layer, H3, that is explicitly designed for better recalling earlier tokens in the sequence and comparing tokens across the sequence. Furthermore, we introduce FlashConv, a fused block FFT algorithm that improves efficiency on sequences up to 8K and introduces a novel state passing algorithm that exploits the recurrent properties of SSMs to scale to longer sequences.
Using FlashConv, we are able to scale hybrid H3-attention language models up to 2.7B parameters on the Pile benchmark and achieve promising initial results, outperforming Transformers in zero- and few-shot learning on a majority of tasks in the SuperGLUE benchmark. We also evaluate how well FlashConv speeds up SSMs and demonstrate nearly linear scaling from 256 to 1024 tokens with only a small increase in memory usage.
Summarizing https://arxiv.org/pdf/2006.16236.pdf
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Transformers are a type of neural network architecture that uses self-attention mechanisms to process sequential data. They have been shown to achieve state-of-the-art performance on a wide range of natural language processing tasks, including machine translation, text classification, and question answering. However, their computational complexity can be prohibitive for long sequences or large datasets. In this paper, we introduce the Linear Transformer, which reduces the memory footprint and scales linearly with respect to context length by using kernel-based formulation of self-attention and associative property of matrix products to calculate self-attention weights. We also express causal masking with linear complexity and constant memory, revealing the relation between transformers and RNNs, enabling them to perform autoregressive inference orders of magnitude faster.
The paper also discusses how existing works seek to improve memory efficiency in transformers through weight pruning (Michel et al., 2019), weight factorization (Lan et al., 2020), weight quantization (Zafrir et al., 2019) or knowledge distillation. Clark et al. (2020) proposed a progressive fashion. The authors first trained a large model on a small dataset, then gradually increased the size of the model while keeping the number of parameters constant by adding more layers and increasing the hidden state size. This approach allows for better utilization of the available resources and reduces the risk of overfitting.
In summary, transformers have revolutionized natural language processing tasks with their ability to process long sequences efficiently. However, their computational complexity can be prohibitive for large datasets or long sequences. Linear Transformers address this issue by using kernel-based formulation of self-attention and associative property of matrix products to calculate self-attention weights, resulting in linear memory footprint and constant time complexity. The paper also discusses how existing works seek to improve memory efficiency in transformers through weight pruning, weight factorization, weight quantization or knowledge distillation.
Summarizing https://arxiv.org/pdf/2307.08691.pdf
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The paper proposes a new attention mechanism called FlashAttention-2 that addresses the challenges of slow convergence and limited parallelism in deep learning models. The proposed method uses better work partitioning and parallelization to reduce the time complexity of attention computation. The authors provide experimental results on various benchmarks to demonstrate the effectiveness of their approach compared to other state-of-the-art methods. In addition, they introduce a scheduling strategy for the outer loop (over sequence length) that is efficient when this number is large, since we can effectively use almost all of the compute resources on the GPU. They also suggest parallelizing over the sequence length dimension, which leads to significant speedup for long sequences with small batch sizes or small number of heads.
The paper also presents empirical validation of FlashAttention-2 by comparing it to standard implementations and other state-of-the-art attention mechanisms. The results show that FlashAttention-2 is 1.7-3.0× faster than FlashAttention, 1.3-2.5× faster than FlashAttention in Triton, and 3-10× faster than a standard attention implementation. The authors also demonstrate that FlashAttention-2 reaches convergence faster than the baseline methods, which is particularly important when dealing with long sequences.
Overall, this paper provides an interesting contribution to the field of deep learning, specifically in the area of attention mechanisms. It proposes a new method for improving the efficiency of attention computation while maintaining its effectiveness. The experimental results show that FlashAttention-2 outperforms other state-of-the-art methods in terms of speed and convergence rate, making it a promising approach for future research in this area.
Summarizing https://arxiv.org/pdf/2307.08621.pdf
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This paper proposes RETNET, a foundation architecture for large language models that achieves training parallelism, low-cost deployment, and good performance. The proposed mechanism can be written as recurrent neural networks or parallel representation, which is favorable for inference. Experimental results show that RETNET outperforms the Transformer model in terms of scaling, parallel training, low-cost deployment, and efficient inference.
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Chain Of Verification (COV) is a new technique that combines prompting and consistency checks made by the LLM to reduce hallucination in large language models. It focuses on cases where hallucinations are frequent such as lack of context, complex queries, and long conversations. COV generates a baseline answer, plans verifications, fact-checks, and produces a final, verified response. The revised answer for the question "What are the risks for SME regarding their tech?" includes more facts and is of higher quality than the baseline answer.
Summarizing https://eprint.iacr.org/2020/852.pdf
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FROST is a new threshold signature scheme that provides efficient and flexible threshold signatures for blockchain systems. It uses Schnorr signatures and achieves high efficiency and security while also allowing for flexible threshold parameters. The authors demonstrate its performance and security through extensive analysis and experiments on real-world datasets. Unlike signatures in a single-party setting, threshold signatures require cooperation among a threshold number of signers each holding a share of a common private key. Consequently, generating signatures in a threshold setting imposes overhead due to network rounds among signers, proving costly when secret shares are stored on network-limited devices or when coordination occurs over unreliable networks. In this work, we present FROST, a Flexible Round-Optimized Schnorr Threshold signature scheme that reduces network overhead during signing operations while employing a novel technique to protect against forgery attacks applicable to similar schemes in the literature. FROST improves upon the state of the art in Schnorr threshold signature protocols, as it can safely perform signing operations in a single round without limiting concurrency of signing operations, yet allows for true threshold signing, such that only a threshold t out of n possible participants must sign to authenticate a message. Additionally, FROST provides efficient and flexible threshold parameters, allowing users to choose their desired level of security and efficiency based on their specific use case. We demonstrate the performance and security of FROST through extensive analysis and experiments on real-world datasets, showing its superiority over existing threshold signature schemes in terms of both efficiency and security.
Summarizing https://arxiv.org/pdf/2208.04933.pdf
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We introduce Simplified State Space Layers for Sequence Modeling, which are designed to improve performance on long-range sequence modeling tasks. The S5 layer builds on the S4 layer by using one multi-input, multi-output (MIMO) SSM instead of many independent single-input, single-output (SISO) SSMs, and an efficient parallel scan. This results in a state space layer that can match the computational efficiency of S4 while achieving state-of-the-art performance on several long-range sequence modeling tasks. We compare empirically the performance of the S5 layer to the S4 layer and other baseline methods on the Long Range Arena benchmark, showing that the S5 layer matches the performance and efficiency of the S4 layer, with the highest score among all models on the Path-X task.
We also introduce Simplified State Space Layers for Sequence Modeling, which are designed to improve performance on raw speech classification tasks. The S5 layer builds on the S4 layer by using one multi-input, multi-output (MIMO) SSM instead of many independent single-input, single-output (SISO) SSMs, and an efficient parallel scan. This results in a state space layer that can match the computational efficiency of S4 while achieving state-of-the-art performance on several raw speech classification tasks. We compare empirically the performance of the S5 layer to the S4 layer and other baseline methods on the LibriSpeech benchmark, showing that the S5 layer matches the performance and efficiency of the S4 layer, with the highest score among all models on the 100-hour task.
We also introduce Simplified State Space Layers for Sequence Modeling, which are designed to improve performance on text generation tasks. The S5 layer builds on the S4 layer by using one multi-input, multi-output (MIMO) SSM instead of many independent single-input, single-output (SISO) SSMs, and an efficient parallel scan. This results in a state space layer that can match the computational efficiency of S4 while achieving state-of-the-art performance on several text generation tasks. We compare empirically the performance of the S5 layer to the S4 layer and other baseline methods on the GPT-2 benchmark, showing that the S5 layer matches the performance and efficiency of the S4 layer, with the highest score among all models on the 100-hour task.
Overall, our experiments demonstrate that Simplified State Space Layers are a powerful tool for building efficient and effective sequence modeling architectures, and we hope they will inspire further research in this direction.
Summarizing https://arxiv.org/pdf/2203.14343.pdf
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The proposed Deep State Space (DSS) layer is an effective alternative to Structured State Spaces (S4) for modeling long-range dependencies in sequential data. The DSS layer matches the performance of S4 on several tasks across various modalities without requiring low rank correction or being conceptually simpler to implement. Our code is available at <https://github.com/ag1988/dss>.
To investigate the factors contributing to its performance, we performed an ablation analysis and found that:
* Initializing Λ randomly works just as well as using Skew-Hippo initialization.
* Restricting DSS to only model local interactions does not hurt its performance on the above tasks.
* Truncating the kernel constructed in DSSSOFTMAX (Algorithm 1) to a shorter length than the input size does not significantly affect its performance.
We also analyzed the learned parameters of DSS and found that it captures long-range dependencies effectively by learning non-linear kernels with varying
Summarizing https://arxiv.org/pdf/2303.06349.pdf
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ListOps
λj=exp(−exp(νlogj)+iexp(θlogj))γj←(1−|λj|2)1/2magnitudephase
Tanh-RNN
aligned with S4/5
PathFinde%
skip connection
time pool
90%
90%
Stable
Deep RNNs (the S4 way)
NRrm
6tDble
LLn-5NN
MLP/GLU(same for all timestamps)
70%
70%
Summarizing https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/
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This paper presents the development and release of Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. The authors also provide a detailed description of their approach to fine-tune and safety improvements of Llama 2-Chat for dialogue use cases.
Summarizing https://arxiv.org/pdf/2212.08751.pdf
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The paper proposes a method for generating 3D objects using text prompts that produces 3D models in only 1-2 minutes on a single GPU. The method uses two diffusion models - one for generating synthetic views from the text prompts and another for producing 3D point clouds based on the generated image. While the sample quality is not as good as state-of-the-art methods, it offers practical trade-offs for some use cases. To ensure that we always sample in-distribution renders (rather than only sampling them 5% of the time), we add a special token to every 3D render’s text prompt indicating that it is a 3D render; we then sample with this token at test time. Finally, we employed various heuristics to reduce the frequency of low-quality models in our dataset. First, we eliminated flat objects by computing the SVD of each point cloud and only retaining those where the smallest singular value was above a certain threshold. Next, we clustered the dataset by CLIP features (for each object, we averaged features over all renders). We found that some clusters contained many low-quality categories of models, while others
Summarizing https://arxiv.org/pdf/2105.14103.pdf
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The authors introduce Attention Free Transformers (AFT), an efficient variant of Transformers that eliminates the need for self-attention. In AFT layers, the key and value are first combined with a set of learned position biases, resulting in an element-wise multiplication with the query. This new operation has a memory complexity linear with respect to both the context size and the dimension of features, making it compatible with large input and model sizes. The authors also introduce two model variants, AFT-local and AFT-conv, which take advantage of the idea of locality and spatial weight sharing while maintaining global connectivity. They conduct extensive experiments on three tasks: autoregressive image modeling, character-level language modeling, and image classification. The results show that AFT demonstrates competitive performance on all benchmarks, while providing excellent efficiency at the same time.
The authors compare their approach to previous work in efficient attention mechanisms such as Reformers [8] and Sparse Transformers [7], which apply LSH or fixed sparse/local context patterns. They also mention ImageTransformer [17] and Attention models in vision tasks (often combined with convolutions) that use image
Summarizing https://arize.com/blog/lost-in-the-middle-how-language-models-use-long-contexts-paper-reading/
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This study examines how well language models utilize longer input contexts for multi-document question answering and key-value retrieval tasks. The researchers find that performance is highest when relevant information is at the beginning or end of the context, but accessing information in the middle of long contexts leads to significant performance degradation. Even explicitly long-context models experience decreased performance as the context length increases. This analysis enhances our understanding and offers new evaluation protocols for future long-context models.
Summarizing https://arxiv.org/pdf/1609.09106.pdf
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In this text, we introduce a new approach to training neural networks using hypernetworks. In this method, the input is an embedding vector that describes the entire weights of a given layer in the main network. The embedding vectors can be fixed parameters that are also learned during end-to-end training, allowing for approximate weight sharing within and across layers of the main network. This approach has been applied to deep convolutional networks and long recurrent networks, where hypernetworks can be viewed as a relaxed form of weight-sharing across layers. The results show that hypernetworks applied to convolutional networks still achieve respectable results for image recognition tasks while requiring fewer learnable parameters compared to state-of-the-art baseline models.
For this text:
Additionally, embedding vectors can also be generated dynamically by your hypernetwork, allowing the weights of a current network to change over time steps and also adapt to the input sequence. We perform experiments to investigate the behavior of hypernetworks in a range of contexts and find that they mix well with other techniques such as batch normalization and layer normalization. Our main result is that hypernetworks can generate non-shared weights for LSTM that work better than the standard version on some tasks, but not always.
Summarizing https://arxiv.org/pdf/2208.12242.pdf
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DreamBooth is a text-based image generation system capable of generating images of subjects in different contexts based on input images and text prompts. The system uses a combination of computer vision techniques and natural language processing to create realistic and varied images of people or objects. The goal of this work is to expand the language-vision dictionary of the model such that it can bind new words with specific subjects the user wants to generate. This approach allows for personalized image generation, preserving key visual features while creating novel scenes and interactions.
The proposed evaluation protocol measures subject fidelity and prompt fidelity of generated results. We make our dataset and evaluation protocol publicly available on the project webpage.
We apply our approach to various text-based image generation applications including recontextualization of subjects, modification of their properties, original art renditions, and more, paving the way to a new stream of previously unassailable tasks. We highlight the contribution of each component in our method via ablation studies, and compare with alternative baselines and related work. We also conduct a user study to evaluate subject and prompt fidelity in our synthesized images, compared to alternative approaches.
Summarizing https://arxiv.org/pdf/2106.09685.pdf
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LoRA (Low-Rank Adaptation) is a method for adapting large language models to low-rank structures, which can improve the efficiency and accuracy of language understanding tasks such as machine translation and question answering. The paper describes how LoRA works, including the use of a novel regularization term that encourages the model to learn low-rank representations, and provides experimental results demonstrating its effectiveness on several benchmark datasets. One key advantage of LoRA is that it allows for efficient task switching by sharing pre-trained models and using them to build many small LoRA modules for different tasks. This reduces storage requirements and task-switching overhead significantly. Additionally, LoRA makes training more efficient and lowers the hardware barrier to entry by up to 3 times when using adaptive optimizers since we only optimize injected, much smaller low-rank matrices. Finally, LoRA's simple linear design allows for merging trainable matrices with frozen weights during deployment without introducing any inference latency compared to a fully fine-tuned model.
In contrast, adapter layers introduce inference latency due to their sequential processing, which can be problematic in online inference settings where the batch size is typically as small as one. Furthermore, adapters require additional parameters and computation, which can increase the memory footprint and training time of the overall model.
Overall, LoRA provides a flexible and efficient approach to adapting large language models to low-rank structures, making it an attractive option for a wide range of NLP tasks.
Summarizing https://arxiv.org/pdf/2307.06949.pdf
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HyperDreamBooth is a new approach for personalizing text-to-image models that can generate images based on input text descriptions. The authors demonstrate that their method is much faster than previous methods while still maintaining high quality results. They propose HyperDreamBooth, a hypernetwork capable of efficiently generating a small set of personalized weights from a single image of a person. By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications.
Their method achieves personalization on faces in roughly 20 seconds, 25x faster than DreamBooth and 125x faster than Textual Inversion, using as few as one reference image, with the same quality and style diversity as DreamBooth. Also their method yields a model that is 10000x smaller than a normal DreamBooth model.
The authors demonstrate the effectiveness of their approach by comparing it to previous methods such as DreamBooth and Textual Inversion. They show that their method can generate high-quality images of people's faces in various contexts and styles while also preserving the model's crucial knowledge of diverse styles and semantic modifications.
Overall, this paper presents an efficient and effective method for personalizing text-to-image models using a hypernetwork capable of generating a small set of personalized weights from a single image of a person. The proposed method achieves personalization on faces in roughly 20 seconds, 25x faster than DreamBooth and 125x faster than Textual Inversion, using as few as one reference image, with the same quality and style diversity as DreamBooth.
Summarizing https://www.nytimes.com/2023/07/02/science/ai-mathematics-machine-learning.html
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The article discusses the importance of having a good credit score, which is essential for getting approved for loans, credit cards, and other financial products. It also highlights the impact that a low credit score can have on one's ability to access credit and how it can affect their overall financial health. The article provides tips on how to improve your credit score, including paying bills on time, keeping credit utilization low, and checking credit reports regularly. Additionally, the article emphasizes the importance of building a strong credit history over time by using credit responsibly and making timely payments.
Summarizing https://www.quantamagazine.org/an-enormous-gravity-hum-moves-through-the-universe-20230628/
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The Green Bank Telescope has detected a low hum rumbling through the universe caused by supermassive black hole collisions. This discovery promises to reveal previously hidden phenomena such as the cosmic whirling of black holes or exotic celestial specters. The other place to start the story is in 1967, with a graduate student from Lurgan, Northern Ireland, named Jocelyn Bell. Using a radio telescope that she helped build near Cambridge, U.K., she spotted an unusual signal in space that repeated every second. She and other astronomers later classified these signals as a new class of celestial object known as pulsars — the rapidly spinning cores of dead stars. Today, some are known to spin exceedingly fast, emitting regular pulses of radio waves hundreds or even thousands of times per second.
The stopwatch-like regularity of pulsars makes them valuable cosmic timekeepers. In 1983, the U.S. astronomers Ron Hellings and George Downs suggested a novel way to put them to use: If gravitational waves were squeezing and stretching space-time, that motion would cause the distance between two orbiting pulsars to change slightly over time. By precisely measuring this distance, scientists could detect the elusive ripples in the fabric of space-time predicted by Einstein's theory of general relativity.
This idea led to the construction of the first dedicated gravitational wave detector, known as the Laser Interferometer Gravitational-Wave Observatory (LIGO). The first LIGO detector was built in Hanford, Washington, in 1994, with a second one following in Livingston, Louisiana, in 2002. These instruments use lasers to measure the distance between two mirrors that are precisely aligned and separated by several kilometers. If a gravitational wave passes through the detector, it will cause the distance between the mirrors to change slightly, which can be detected by measuring the interference pattern of light waves.
In 2015, the LIGO detectors made history when they announced the first-ever direct detection of gravitational waves from the collision of two black holes. This discovery was the culmination of decades of work by generations of scientists and engineers who had dedicated their careers to this quest. The detection of gravitational waves has opened up a new window on the universe, allowing us to study some of the most violent and exotic phenomena in the cosmos.