We thank the reviewers for insightful remarks and comments that help to considerably improve our manuscript. We1 address the most important ones in detail below. Before doing so, we highlight a comment from R3 in order to make an2 important clarification about the scope of our contribution. "It is well known that an attention mechanism would reduce3 gradient vanishing. It feels trivial to me as there is a direct connection for gradients to pass. We are in complete agreement and recognize that the very mechanism of (self-)attention was designed to improve6 gradient propagation over long sequences, and that sparsity is a good way to keep complexity costs low. Much like work from the '90s established formal results for gradient exploding/vanishing in deep/recurrent networks, we9 believe it is crucial to establish similar theoretical tools for attention mechanisms, as these methods are under intense10 development where scalability and complexity are important issues. The proposed relevancy mechanism and accompanying experiments,14 building on established work, are meant to illustrate how our theorems can be concretely exploited. We chose simple15 tasks for their ease of interpretation, and their variety of computational demands (memorization, prediction, RL, etc.).16 As is clearly indicated in the text, it is not our goal to propose this method "as is" in a race for state-of-the-art. Werecognize thatreviewersmay have basedtheir evaluation asthey wouldhavein amethod paper, and we20 kindly invite them to reconsider the value of our experiments in the broader context of our theoretical contributions. We21 also thank reviewers for their additional minor comments not explicitly addressed here and agree to implement them.22 R1: Q"The authors didn't spell out the relation between κ and d: higher κ tends to have smaller d.

Neural networks augmented with external memory have the ability to learn algorithmic solutions to complex tasks.

Things to Do in L.A. Tap to enable a layout that focuses on the article. One of L.A.'s most popular hiking spots is getting bathrooms. This is read by an automated voice. Please report any issues or inconsistencies here . The $1-million project will draw odors, foot traffic, homeless people and other problems, opponents say.

Biological and psychological concepts have inspired reinforcement learning algorithms to create new complex behaviors that expand agents' capacity. These behaviors can be seen in the rise of techniques like goal decomposition, curriculum, and intrinsic rewards, which have paved the way for these complex behaviors. One limitation in evaluating these methods is the requirement for engineered extrinsic for realistic environments. A central challenge in engineering the necessary reward function(s) comes from these environments containing states that carry high negative rewards, but provide no feedback to the agent. Death is one such stimuli that fails to provide direct feedback to the agent. In this work, we introduce an intrinsic reward function inspired by early amygdala development and produce this intrinsic reward through a novel memory-augmented neural network (MANN) architecture. We show how this intrinsic motivation serves to deter exploration of terminal states and results in avoidance behavior similar to fear conditioning observed in animals. Furthermore, we demonstrate how modifying a threshold where the fear response is active produces a range of behaviors that are described under the paradigm of general anxiety disorders (GADs). We demonstrate this behavior in the Miniworld Sidewalk environment, which provides a partially observable Markov decision process (POMDP) and a sparse reward with a non-descriptive terminal condition, i.e., death. In effect, this study results in a biologically-inspired neural architecture and framework for fear conditioning paradigms; we empirically demonstrate avoidance behavior in a constructed agent that is able to solve environments with non-descriptive terminal conditions.

In this study, we approach morphological productivity from two perspectives: a cognitive-computational perspective, and a diachronic perspective zooming in on an actual speaker, Thomas Mann. For developing the first perspective, we make use of a cognitive computational model of the mental lexicon, the discriminative lexicon model. For computational mappings between form and meaning to be productive, in the sense that novel, previously unencountered words, can be understood and produced, there must be systematicities between the form space and the semantic space. If the relation between form and meaning would be truly arbitrary, a model could memorize form and meaning pairings, but there is no way in which the model would be able to generalize to novel test data. For Finnish nominal inflection, Malay derivation, and English compounding, we explore, using the Discriminative Lexicon Model as a computational tool, to trace differences in the degree to which inflectional and word formation patterns are productive. We show that the DLM tends to associate affix-like sublexical units with the centroids of the embeddings of the words with a given affix. For developing the second perspective, we study how the intake and output of one prolific writer, Thomas Mann, changes over time. We show by means of an examination of what Thomas Mann is likely to have read, and what he wrote, that the rate at which Mann produces novel derived words is extremely low. There are far more novel words in his input than in his output. We show that Thomas Mann is less likely to produce a novel derived word with a given suffix the greater the average distance is of the embeddings of all derived words to the corresponding centroid, and discuss the challenges of using speaker-specific embeddings for low-frequency and novel words.

Additional Feedback: - Line 145, how can Theorem 1 be related to the early attention mechanism [1]? As the attention weights are computed adaptively, it is unlikely that they are uniform. MANNs learn to store relevant hidden states to a fixed-size memory, which seems to have the same purpose as relevancy screening mechanism. What is the advantage of the proposed method over MANNs? How are MANNs related to the Theorem 2? - The paper neglects prior works that also aim to quantify gradient propagation in RNNs and attentive models [4,5].

While memory-augmented neural networks (MANNs) offer an effective solution for few-shot learning (FSL) by integrating deep neural networks with external memory, the capacity requirements and energy overhead of data movement become enormous due to the large number of support vectors in many-class FSL scenarios. Various in-memory search solutions have emerged to improve the energy efficiency of MANNs. NAND-based multi-bit content addressable memory (MCAM) is a promising option due to its high density and large capacity. Despite its potential, MCAM faces limitations such as a restricted number of word lines, limited quantization levels, and non-ideal effects like varying string currents and bottleneck effects, which lead to significant accuracy drops. To address these issues, we propose several innovative methods. First, the Multi-bit Thermometer Code (MTMC) leverages the extensive capacity of MCAM to enhance vector precision using cumulative encoding rules, thereby mitigating the bottleneck effect. Second, the Asymmetric vector similarity search (AVSS) reduces the precision of the query vector while maintaining that of the support vectors, thereby minimizing the search iterations and improving efficiency in many-class scenarios. Finally, the Hardware-Aware Training (HAT) method optimizes controller training by modeling the hardware characteristics of MCAM, thus enhancing the reliability of the system. Our integrated framework reduces search iterations by up to 32 times, and increases overall accuracy by 1.58% to 6.94%.

Neural networks augmented with external memory have the ability to learn algorithmic solutions to complex tasks. These models appear promising for applications such as language modeling and machine translation. However, they scale poorly in both space and time as the amount of memory grows -- limiting their applicability to real-world domains. Here, we present an end-to-end differentiable memory access scheme, which we call Sparse Access Memory (SAM), that retains the representational power of the original approaches whilst training efficiently with very large memories. We show that SAM achieves asymptotic lower bounds in space and time complexity, and find that an implementation runs 1,000 faster and with 3,000 less physical memory than non-sparse models. SAM learns with comparable data efficiency to existing models on a range of synthetic tasks and one-shot Omniglot character recognition, and can scale to tasks requiring 100,000s of time steps and memories. As well, we show how our approach can be adapted for models that maintain temporal associations between memories, as with the recently introduced Differentiable Neural Computer.

Conformers have recently been proposed as a promising modelling approach for automatic speech recognition (ASR), outperforming recurrent neural network-based approaches and transformers. Nevertheless, in general, the performance of these end-to-end models, especially attention-based models, is particularly degraded in the case of long utterances. To address this limitation, we propose adding a fully-differentiable memory-augmented neural network between the encoder and decoder of a conformer. This external memory can enrich the generalization for longer utterances since it allows the system to store and retrieve more information recurrently. Notably, we explore the neural Turing machine (NTM) that results in our proposed Conformer-NTM model architecture for ASR. Experimental results using Librispeech train-clean-100 and train-960 sets show that the proposed system outperforms the baseline conformer without memory for long utterances.

As far back as the 19th century, soothsayers have been promising and warning against it in equal measure. While we have yet to achieve a post-scarcity utopia or descend into a robot-ruled wasteland, year upon year, little by little, many of those predictions have jumped from the pages of sci-fi novels and into news headlines as ever-increasing computing power turns future fantasies into tangible reality. From law enforcement to medicine and visual arts to weaponry, the real-world impacts of AI are already being felt. Tech's best and brightest are hard at work trying to streamline the songwriting process or replace it altogether: Splice's Similar Sounds uses AI to scan thousands of samples before offering the best kick to complement your snare; Orb's Producer Suite generates rhythms, melodies and chord progressions to help you get started on a track; and services like Amper need only a few keywords to create fully realised background music. So, are composers and songwriters staring into the void of their own obsolescence?