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Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | rypescdivide | \cite{rypescdivide} | Divide and not forget: Ensemble of selectively trained experts in
Continual Learning | http://arxiv.org/abs/2401.10191v3 | Class-incremental learning is becoming more popular as it helps models widen
their applicability while not forgetting what they already know. A trend in
this area is to use a mixture-of-expert technique, where different models work
together to solve the task. However, the experts are usually trained all at
once using w... | true | true | Rype{\'s}{\'c}, Grzegorz and Cygert, Sebastian and Khan, Valeriya and Trzcinski, Tomasz and Zieli{\'n}ski, Bartosz Micha{\l} and Twardowski, Bart{\l}omiej | 2,024 | null | null | null | null | Divide and not forget: Ensemble of selectively trained experts in
Continual Learning | Ensemble of selectively trained experts in Continual Learning - arXiv | https://arxiv.org/abs/2401.10191 | Divide and not forget: Ensemble of selectively trained experts in Continual Learning. Authors:Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan, |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | kirkpatrick2017overcoming | \cite{kirkpatrick2017overcoming} | Overcoming catastrophic forgetting in neural networks | http://arxiv.org/abs/1612.00796v2 | The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this lim... | true | true | Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and others | 2,017 | null | null | null | Proceedings of the national academy of sciences | Overcoming catastrophic forgetting in neural networks | Overcoming catastrophic forgetting in neural networks | http://arxiv.org/pdf/1612.00796v2 | The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this lim... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | magistrielastic | \cite{magistrielastic} | Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental
Learning | http://arxiv.org/abs/2402.03917v3 | Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a
sequence of tasks without having access to previous task data. In this paper,
we consider the challenging Cold Start scenario in which insufficient data is
available in the first task to learn a high-quality backbone. This is
especially challenging f... | true | true | Magistri, Simone and Trinci, Tomaso and Soutif, Albin and van de Weijer, Joost and Bagdanov, Andrew D | 2,024 | null | null | null | null | Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental
Learning | [2402.03917] Elastic Feature Consolidation for Cold Start Exemplar ... | https://arxiv.org/abs/2402.03917 | Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | saha2021gradient | \cite{saha2021gradient} | Gradient Projection Memory for Continual Learning | http://arxiv.org/abs/2103.09762v1 | The ability to learn continually without forgetting the past tasks is a
desired attribute for artificial learning systems. Existing approaches to
enable such learning in artificial neural networks usually rely on network
growth, importance based weight update or replay of old data from the memory.
In contrast, we propo... | true | true | Saha, Gobinda and Garg, Isha and Roy, Kaushik | 2,021 | null | null | null | null | Gradient Projection Memory for Continual Learning | Gradient Projection Memory for Continual Learning | http://arxiv.org/pdf/2103.09762v1 | The ability to learn continually without forgetting the past tasks is a
desired attribute for artificial learning systems. Existing approaches to
enable such learning in artificial neural networks usually rely on network
growth, importance based weight update or replay of old data from the memory.
In contrast, we propo... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | lin2022trgp | \cite{lin2022trgp} | TRGP: Trust Region Gradient Projection for Continual Learning | http://arxiv.org/abs/2202.02931v1 | Catastrophic forgetting is one of the major challenges in continual learning.
To address this issue, some existing methods put restrictive constraints on the
optimization space of the new task for minimizing the interference to old
tasks. However, this may lead to unsatisfactory performance for the new task,
especially... | true | true | Lin, Sen and Yang, Li and Fan, Deliang and Zhang, Junshan | 2,022 | null | null | null | International Conference on Learning Representations(ICLR) | TRGP: Trust Region Gradient Projection for Continual Learning | TRGP: Trust Region Gradient Projection for Continual Learning | http://arxiv.org/pdf/2202.02931v1 | Catastrophic forgetting is one of the major challenges in continual learning.
To address this issue, some existing methods put restrictive constraints on the
optimization space of the new task for minimizing the interference to old
tasks. However, this may lead to unsatisfactory performance for the new task,
especially... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | cbrs2020 | \cite{cbrs2020} | Online continual learning from imbalanced data | null | null | true | false | Chrysakis, Aristotelis and Moens, Marie-Francine | 2,020 | null | null | null | null | Online continual learning from imbalanced data | Online Continual Learning from Imbalanced Data | https://proceedings.mlr.press/v119/chrysakis20a.html | We aim to evaluate memory population methods that are used in online continual learning, when dealing with highly imbalanced and temporally correlated streams |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | infors2022 | \cite{infors2022} | Information-theoretic Online Memory Selection for Continual Learning | http://arxiv.org/abs/2204.04763v1 | A challenging problem in task-free continual learning is the online selection
of a representative replay memory from data streams. In this work, we
investigate the online memory selection problem from an information-theoretic
perspective. To gather the most information, we propose the \textit{surprise}
and the \textit{... | true | true | Sun, Shengyang and Calandriello, Daniele and Hu, Huiyi and Li, Ang and Titsias, Michalis | 2,022 | null | null | null | null | Information-theoretic Online Memory Selection for Continual Learning | Information-theoretic Online Memory Selection for Continual Learning | https://openreview.net/forum?id=IpctgL7khPp | We present information-theoretic algorithms to tackle the online memory selection problem in task-free and data imbalanced continual learning. |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | shin2017continual | \cite{shin2017continual} | Continual Learning with Deep Generative Replay | http://arxiv.org/abs/1705.08690v3 | Attempts to train a comprehensive artificial intelligence capable of solving
multiple tasks have been impeded by a chronic problem called catastrophic
forgetting. Although simply replaying all previous data alleviates the problem,
it requires large memory and even worse, often infeasible in real world
applications wher... | true | true | Shin, Hanul and Lee, Jung Kwon and Kim, Jaehong and Kim, Jiwon | 2,017 | null | null | null | Advances in Neural Information Processing Systems(Neurips) | Continual Learning with Deep Generative Replay | Continual Learning with Deep Generative Replay | http://arxiv.org/pdf/1705.08690v3 | Attempts to train a comprehensive artificial intelligence capable of solving
multiple tasks have been impeded by a chronic problem called catastrophic
forgetting. Although simply replaying all previous data alleviates the problem,
it requires large memory and even worse, often infeasible in real world
applications wher... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | chaudhry2018efficient | \cite{chaudhry2018efficient} | Efficient Lifelong Learning with A-GEM | null | null | true | false | Chaudhry, Arslan and Ranzato, Marc’Aurelio and Rohrbach, Marcus and Elhoseiny, Mohamed | 2,018 | null | null | null | null | Efficient Lifelong Learning with A-GEM | Efficient Lifelong Learning with A-GEM | http://arxiv.org/pdf/1812.00420v2 | In lifelong learning, the learner is presented with a sequence of tasks,
incrementally building a data-driven prior which may be leveraged to speed up
learning of a new task. In this work, we investigate the efficiency of current
lifelong approaches, in terms of sample complexity, computational and memory
cost. Towards... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | chaudhry2019continual | \cite{chaudhry2019continual} | Continual learning with tiny episodic memories | null | null | true | false | Dokania, P and Torr, P and Ranzato, M | 2,019 | null | null | null | null | Continual learning with tiny episodic memories | [PDF] Continual Learning with Tiny Episodic Memories | https://tajanthan.github.io/il/docs/cler.pdf | In this work, we empirically analyze the effective- ness of a very small episodic memory in a CL setup where each training example is only seen once. |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | rebuffi2017icarl | \cite{rebuffi2017icarl} | iCaRL: Incremental Classifier and Representation Learning | http://arxiv.org/abs/1611.07725v2 | A major open problem on the road to artificial intelligence is the
development of incrementally learning systems that learn about more and more
concepts over time from a stream of data. In this work, we introduce a new
training strategy, iCaRL, that allows learning in such a class-incremental way:
only the training dat... | true | true | Rebuffi, Sylvestre-Alvise and Kolesnikov, Alexander and Sperl, Georg and Lampert, Christoph H | 2,017 | null | null | null | null | iCaRL: Incremental Classifier and Representation Learning | iCaRL: Incremental Classifier and Representation Learning | http://arxiv.org/pdf/1611.07725v2 | A major open problem on the road to artificial intelligence is the
development of incrementally learning systems that learn about more and more
concepts over time from a stream of data. In this work, we introduce a new
training strategy, iCaRL, that allows learning in such a class-incremental way:
only the training dat... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | gargtic2024 | \cite{gargtic2024} | TiC-CLIP: Continual Training of CLIP Models | null | null | true | false | Garg, Saurabh and Farajtabar, Mehrdad and Pouransari, Hadi and Vemulapalli, Raviteja and Mehta, Sachin and Tuzel, Oncel and Shankar, Vaishaal and Faghri, Fartash | 2,024 | null | null | null | null | TiC-CLIP: Continual Training of CLIP Models | TiC-CLIP: Continual Training of CLIP Models | http://arxiv.org/pdf/2310.16226v3 | Keeping large foundation models up to date on latest data is inherently
expensive. To avoid the prohibitive costs of constantly retraining, it is
imperative to continually train these models. This problem is exacerbated by
the lack of any large scale continual learning benchmarks or baselines. We
introduce the first se... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | lopez2017gradient | \cite{lopez2017gradient} | Gradient Episodic Memory for Continual Learning | http://arxiv.org/abs/1706.08840v6 | One major obstacle towards AI is the poor ability of models to solve new
problems quicker, and without forgetting previously acquired knowledge. To
better understand this issue, we study the problem of continual learning, where
the model observes, once and one by one, examples concerning a sequence of
tasks. First, we ... | true | true | Lopez-Paz, David and Ranzato, Marc'Aurelio | 2,017 | null | null | null | Advances in Neural Information Processing Systems(Neurips) | Gradient Episodic Memory for Continual Learning | Gradient Episodic Memory for Continual Learning | http://arxiv.org/pdf/1706.08840v6 | One major obstacle towards AI is the poor ability of models to solve new
problems quicker, and without forgetting previously acquired knowledge. To
better understand this issue, we study the problem of continual learning, where
the model observes, once and one by one, examples concerning a sequence of
tasks. First, we ... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | bennani2020generalisation | \cite{bennani2020generalisation} | Generalisation Guarantees for Continual Learning with Orthogonal
Gradient Descent | http://arxiv.org/abs/2006.11942v4 | In Continual Learning settings, deep neural networks are prone to
Catastrophic Forgetting. Orthogonal Gradient Descent was proposed to tackle the
challenge. However, no theoretical guarantees have been proven yet. We present
a theoretical framework to study Continual Learning algorithms in the Neural
Tangent Kernel reg... | true | true | Bennani, Mehdi Abbana and Sugiyama, Masashi | 2,020 | null | null | null | null | Generalisation Guarantees for Continual Learning with Orthogonal
Gradient Descent | Generalisation Guarantees for Continual Learning with Orthogonal ... | https://ui.adsabs.harvard.edu/abs/2020arXiv200611942A/abstract | In Continual Learning settings, deep neural networks are prone to Catastrophic Forgetting. Orthogonal Gradient Descent was proposed to tackle the challenge. |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | doan2021theoretical | \cite{doan2021theoretical} | A Theoretical Analysis of Catastrophic Forgetting through the NTK
Overlap Matrix | http://arxiv.org/abs/2010.04003v2 | Continual learning (CL) is a setting in which an agent has to learn from an
incoming stream of data during its entire lifetime. Although major advances
have been made in the field, one recurring problem which remains unsolved is
that of Catastrophic Forgetting (CF). While the issue has been extensively
studied empirica... | true | true | Doan, Thang and Bennani, Mehdi Abbana and Mazoure, Bogdan and Rabusseau, Guillaume and Alquier, Pierre | 2,021 | null | null | null | null | A Theoretical Analysis of Catastrophic Forgetting through the NTK
Overlap Matrix | A Theoretical Analysis of Catastrophic Forgetting through the NTK ... | https://arxiv.org/abs/2010.04003 | In this paper, we show that the impact of CF increases as two tasks increasingly align. We introduce a measure of task similarity called the NTK overlap matrix. |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | yin2020optimization | \cite{yin2020optimization} | Optimization and Generalization of Regularization-Based Continual
Learning: a Loss Approximation Viewpoint | http://arxiv.org/abs/2006.10974v3 | Neural networks have achieved remarkable success in many cognitive tasks.
However, when they are trained sequentially on multiple tasks without access to
old data, their performance on early tasks tend to drop significantly. This
problem is often referred to as catastrophic forgetting, a key challenge in
continual lear... | true | true | Yin, Dong and Farajtabar, Mehrdad and Li, Ang and Levine, Nir and Mott, Alex | 2,020 | null | null | null | arXiv preprint arXiv:2006.10974 | Optimization and Generalization of Regularization-Based Continual
Learning: a Loss Approximation Viewpoint | Dong Yin - Google Scholar | https://scholar.google.com/citations?user=YtM8P88AAAAJ&hl=en | Optimization and Generalization of Regularization-Based Continual Learning: a Loss Approximation Viewpoint. D Yin, M Farajtabar, A Li, N Levine, A Mott. arXiv |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | cao2022provable | \cite{cao2022provable} | Provable Lifelong Learning of Representations | http://arxiv.org/abs/2110.14098v2 | In lifelong learning, tasks (or classes) to be learned arrive sequentially
over time in arbitrary order. During training, knowledge from previous tasks
can be captured and transferred to subsequent ones to improve sample
efficiency. We consider the setting where all target tasks can be represented
in the span of a smal... | true | true | Cao, Xinyuan and Liu, Weiyang and Vempala, Santosh S | 2,022 | null | null | null | null | Provable Lifelong Learning of Representations | Provable Lifelong Learning of Representations | http://arxiv.org/pdf/2110.14098v2 | In lifelong learning, tasks (or classes) to be learned arrive sequentially
over time in arbitrary order. During training, knowledge from previous tasks
can be captured and transferred to subsequent ones to improve sample
efficiency. We consider the setting where all target tasks can be represented
in the span of a smal... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | li2022provable | \cite{li2022provable} | Provable and Efficient Continual Representation Learning | http://arxiv.org/abs/2203.02026v2 | In continual learning (CL), the goal is to design models that can learn a
sequence of tasks without catastrophic forgetting. While there is a rich set of
techniques for CL, relatively little understanding exists on how
representations built by previous tasks benefit new tasks that are added to the
network. To address t... | true | true | Li, Yingcong and Li, Mingchen and Asif, M Salman and Oymak, Samet | 2,022 | null | null | null | arXiv preprint arXiv:2203.02026 | Provable and Efficient Continual Representation Learning | Provable and Efficient Continual Representation Learning | http://arxiv.org/pdf/2203.02026v2 | In continual learning (CL), the goal is to design models that can learn a
sequence of tasks without catastrophic forgetting. While there is a rich set of
techniques for CL, relatively little understanding exists on how
representations built by previous tasks benefit new tasks that are added to the
network. To address t... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | evron2023continual | \cite{evron2023continual} | Continual Learning in Linear Classification on Separable Data | http://arxiv.org/abs/2306.03534v1 | We analyze continual learning on a sequence of separable linear
classification tasks with binary labels. We show theoretically that learning
with weak regularization reduces to solving a sequential max-margin problem,
corresponding to a special case of the Projection Onto Convex Sets (POCS)
framework. We then develop u... | true | true | Evron, Itay and Moroshko, Edward and Buzaglo, Gon and Khriesh, Maroun and Marjieh, Badea and Srebro, Nathan and Soudry, Daniel | 2,023 | null | null | null | null | Continual Learning in Linear Classification on Separable Data | Continual Learning in Linear Classification on Separable Data | http://arxiv.org/pdf/2306.03534v1 | We analyze continual learning on a sequence of separable linear
classification tasks with binary labels. We show theoretically that learning
with weak regularization reduces to solving a sequential max-margin problem,
corresponding to a special case of the Projection Onto Convex Sets (POCS)
framework. We then develop u... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | evron2022catastrophic | \cite{evron2022catastrophic} | How catastrophic can catastrophic forgetting be in linear regression? | http://arxiv.org/abs/2205.09588v2 | To better understand catastrophic forgetting, we study fitting an
overparameterized linear model to a sequence of tasks with different input
distributions. We analyze how much the model forgets the true labels of earlier
tasks after training on subsequent tasks, obtaining exact expressions and
bounds. We establish conn... | true | true | Evron, Itay and Moroshko, Edward and Ward, Rachel and Srebro, Nathan and Soudry, Daniel | 2,022 | null | null | null | null | How catastrophic can catastrophic forgetting be in linear regression? | How catastrophic can catastrophic forgetting be in linear regression? | http://arxiv.org/pdf/2205.09588v2 | To better understand catastrophic forgetting, we study fitting an
overparameterized linear model to a sequence of tasks with different input
distributions. We analyze how much the model forgets the true labels of earlier
tasks after training on subsequent tasks, obtaining exact expressions and
bounds. We establish conn... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | lin2023theory | \cite{lin2023theory} | Theory on Forgetting and Generalization of Continual Learning | http://arxiv.org/abs/2302.05836v1 | Continual learning (CL), which aims to learn a sequence of tasks, has
attracted significant recent attention. However, most work has focused on the
experimental performance of CL, and theoretical studies of CL are still
limited. In particular, there is a lack of understanding on what factors are
important and how they ... | true | true | Lin, Sen and Ju, Peizhong and Liang, Yingbin and Shroff, Ness | 2,023 | null | null | null | null | Theory on Forgetting and Generalization of Continual Learning | Theory on Forgetting and Generalization of Continual Learning | http://arxiv.org/pdf/2302.05836v1 | Continual learning (CL), which aims to learn a sequence of tasks, has
attracted significant recent attention. However, most work has focused on the
experimental performance of CL, and theoretical studies of CL are still
limited. In particular, there is a lack of understanding on what factors are
important and how they ... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | ding2024understanding | \cite{ding2024understanding} | Understanding Forgetting in Continual Learning with Linear Regression | http://arxiv.org/abs/2405.17583v1 | Continual learning, focused on sequentially learning multiple tasks, has
gained significant attention recently. Despite the tremendous progress made in
the past, the theoretical understanding, especially factors contributing to
catastrophic forgetting, remains relatively unexplored. In this paper, we
provide a general ... | true | true | Ding, Meng and Ji, Kaiyi and Wang, Di and Xu, Jinhui | 2,024 | null | null | null | null | Understanding Forgetting in Continual Learning with Linear Regression | Understanding Forgetting in Continual Learning with Linear Regression | http://arxiv.org/pdf/2405.17583v1 | Continual learning, focused on sequentially learning multiple tasks, has
gained significant attention recently. Despite the tremendous progress made in
the past, the theoretical understanding, especially factors contributing to
catastrophic forgetting, remains relatively unexplored. In this paper, we
provide a general ... |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | goldfarb2023analysis | \cite{goldfarb2023analysis} | Analysis of Catastrophic Forgetting for Random Orthogonal Transformation
Tasks in the Overparameterized Regime | http://arxiv.org/abs/2207.06475v1 | Overparameterization is known to permit strong generalization performance in
neural networks. In this work, we provide an initial theoretical analysis of
its effect on catastrophic forgetting in a continual learning setup. We show
experimentally that in permuted MNIST image classification tasks, the
generalization perf... | true | true | Goldfarb, Daniel and Hand, Paul | 2,023 | null | null | null | null | Analysis of Catastrophic Forgetting for Random Orthogonal Transformation
Tasks in the Overparameterized Regime | [PDF] Analysis of Catastrophic Forgetting for Random Orthogonal ... | https://proceedings.mlr.press/v206/goldfarb23a/goldfarb23a.pdf | Missing: 04/08/2025 |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | zhao2024statistical | \cite{zhao2024statistical} | A Statistical Theory of Regularization-Based Continual Learning | http://arxiv.org/abs/2406.06213v1 | We provide a statistical analysis of regularization-based continual learning
on a sequence of linear regression tasks, with emphasis on how different
regularization terms affect the model performance. We first derive the
convergence rate for the oracle estimator obtained as if all data were
available simultaneously. Ne... | true | true | Zhao, Xuyang and Wang, Huiyuan and Huang, Weiran and Lin, Wei | 2,024 | null | null | null | null | A Statistical Theory of Regularization-Based Continual Learning | [PDF] A Statistical Theory of Regularization-Based Continual Learning | https://openreview.net/pdf?id=A54CXWn9VB | We provide a statistical analysis of regularization- based continual learning on a sequence of linear regression tasks, with emphasis on how differ-. |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | li2024theory | \cite{li2024theory} | Theory on Mixture-of-Experts in Continual Learning | http://arxiv.org/abs/2406.16437v3 | Continual learning (CL) has garnered significant attention because of its
ability to adapt to new tasks that arrive over time. Catastrophic forgetting
(of old tasks) has been identified as a major issue in CL, as the model adapts
to new tasks. The Mixture-of-Experts (MoE) model has recently been shown to
effectively mi... | true | true | Li, Hongbo and Lin, Sen and Duan, Lingjie and Liang, Yingbin and Shroff, Ness B | 2,024 | null | null | null | arXiv preprint arXiv:2406.16437 | Theory on Mixture-of-Experts in Continual Learning | Theory on Mixture-of-Experts in Continual Learning | https://openreview.net/forum?id=7XgKAabsPp | by H Li · Cited by 24 — This paper provides a theoretical study of Mixture-of-Experts (MoE) models for Continual Learning (CL). Specifically, it examines the CL of linear regression |
Unlocking the Power of Rehearsal in Continual Learning: A Theoretical
Perspective | 2506.00205v1 | banayeeanzade2024theoretical | \cite{banayeeanzade2024theoretical} | Theoretical Insights into Overparameterized Models in Multi-Task and
Replay-Based Continual Learning | http://arxiv.org/abs/2408.16939v2 | Multi-task learning (MTL) is a machine learning paradigm that aims to improve
the generalization performance of a model on multiple related tasks by training
it simultaneously on those tasks. Unlike MTL, where the model has instant
access to the training data of all tasks, continual learning (CL) involves
adapting to n... | true | true | Banayeeanzade, Mohammadamin and Soltanolkotabi, Mahdi and Rostami, Mohammad | 2,024 | null | null | null | arXiv preprint arXiv:2408.16939 | Theoretical Insights into Overparameterized Models in Multi-Task and
Replay-Based Continual Learning | Theoretical Insights into Overparameterized Models in Multi-Task... | https://openreview.net/forum?id=4zGPT0ZwnU | The paper provides theoretical insights into multi-task learning (MTL) and replay-based continual learning (CL) in overparameterized settings, using linear |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | Informer | \cite{Informer} | Informer: Beyond Efficient Transformer for Long Sequence Time-Series
Forecasting | http://arxiv.org/abs/2012.07436v3 | Many real-world applications require the prediction of long sequence
time-series, such as electricity consumption planning. Long sequence
time-series forecasting (LSTF) demands a high prediction capacity of the model,
which is the ability to capture precise long-range dependency coupling between
output and input effici... | true | true | Haoyi Zhou and
Shanghang Zhang and
Jieqi Peng and
Shuai Zhang and
Jianxin Li and
Hui Xiong and
Wancai Zhang | 2,021 | null | null | 10.1609/AAAI.V35I12.17325 | null | Informer: Beyond Efficient Transformer for Long Sequence Time-Series
Forecasting | zhouhaoyi/Informer2020: The GitHub repository for the paper ... | https://github.com/zhouhaoyi/Informer2020 | This is the origin Pytorch implementation of Informer in the following paper: Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | Pyraformer | \cite{Pyraformer} | Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time
Series Modeling and Forecasting | null | null | true | false | Shizhan Liu and
Hang Yu and
Cong Liao and
Jianguo Li and
Weiyao Lin and
Alex X. Liu and
Schahram Dustdar | 2,022 | null | https://openreview.net/forum?id=0EXmFzUn5I | null | null | Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time
Series Modeling and Forecasting | Pyraformer: Low-Complexity Pyramidal Attention for Long-Range ... | https://openreview.net/forum?id=0EXmFzUn5I | We propose a multiresolution pyramidal attention mechanism for long-range dependence modeling and time series forecasting. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | LogSparse | \cite{LogSparse} | Enhancing the Locality and Breaking the Memory Bottleneck of Transformer
on Time Series Forecasting | http://arxiv.org/abs/1907.00235v3 | Time series forecasting is an important problem across many domains,
including predictions of solar plant energy output, electricity consumption,
and traffic jam situation. In this paper, we propose to tackle such forecasting
problem with Transformer [1]. Although impressed by its performance in our
preliminary study, ... | true | true | Shiyang Li and
Xiaoyong Jin and
Yao Xuan and
Xiyou Zhou and
Wenhu Chen and
Yu{-}Xiang Wang and
Xifeng Yan | 2,019 | null | https://proceedings.neurips.cc/paper/2019/hash/6775a0635c302542da2c32aa19d86be0-Abstract.html | null | null | Enhancing the Locality and Breaking the Memory Bottleneck of Transformer
on Time Series Forecasting | Enhancing the Locality and Breaking the Memory ... | http://papers.neurips.cc/paper/8766-enhancing-the-locality-and-breaking-the-memory-bottleneck-of-transformer-on-time-series-forecasting.pdf | by S Li · Cited by 2226 — We successfully apply Transformer architecture to time series forecasting and perform extensive experiments on both synthetic and real datasets to validate |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | PatchTST | \cite{PatchTST} | A Time Series is Worth 64 Words: Long-term Forecasting with Transformers | http://arxiv.org/abs/2211.14730v2 | We propose an efficient design of Transformer-based models for multivariate
time series forecasting and self-supervised representation learning. It is
based on two key components: (i) segmentation of time series into
subseries-level patches which are served as input tokens to Transformer; (ii)
channel-independence wher... | true | true | Yuqi Nie and
Nam H. Nguyen and
Phanwadee Sinthong and
Jayant Kalagnanam | 2,023 | null | https://openreview.net/forum?id=Jbdc0vTOcol | null | null | A Time Series is Worth 64 Words: Long-term Forecasting with Transformers | PatchTST (ICLR 2023) - GitHub | https://github.com/yuqinie98/PatchTST | This is an offical implementation of PatchTST: A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. Our model has been included in |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | Crossformer | \cite{Crossformer} | Crossformer: Transformer Utilizing Cross-Dimension Dependency for
Multivariate Time Series Forecasting | null | null | true | false | Yunhao Zhang and
Junchi Yan | 2,023 | null | https://openreview.net/forum?id=vSVLM2j9eie | null | null | Crossformer: Transformer Utilizing Cross-Dimension Dependency for
Multivariate Time Series Forecasting | Crossformer: Transformer Utilizing Cross-Dimension ... | https://openreview.net/forum?id=vSVLM2j9eie | by Y Zhang · Cited by 1238 — We propose Crossformer, a Transformer-based model that explicitly utilizes cross-dimension dependency for multivariate time series forecasting. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | Autoformer | \cite{Autoformer} | Autoformer: Decomposition Transformers with Auto-Correlation for
Long-Term Series Forecasting | http://arxiv.org/abs/2106.13008v5 | Extending the forecasting time is a critical demand for real applications,
such as extreme weather early warning and long-term energy consumption
planning. This paper studies the long-term forecasting problem of time series.
Prior Transformer-based models adopt various self-attention mechanisms to
discover the long-ran... | true | true | Haixu Wu and
Jiehui Xu and
Jianmin Wang and
Mingsheng Long | 2,021 | null | https://proceedings.neurips.cc/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html | null | null | Autoformer: Decomposition Transformers with Auto-Correlation for
Long-Term Series Forecasting | [PDF] Decomposition Transformers with Auto-Correlation for Long-Term ... | https://ise.thss.tsinghua.edu.cn/~mlong/doc/Autoformer-nips21.pdf | Autoformer achieves a 38% relative improvement under the long-term setting on six bench- marks, covering five real-world applications: energy, traffic, |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | FEDformer | \cite{FEDformer} | FEDformer: Frequency Enhanced Decomposed Transformer for Long-term
Series Forecasting | http://arxiv.org/abs/2201.12740v3 | Although Transformer-based methods have significantly improved
state-of-the-art results for long-term series forecasting, they are not only
computationally expensive but more importantly, are unable to capture the
global view of time series (e.g. overall trend). To address these problems, we
propose to combine Transfor... | true | true | Tian Zhou and
Ziqing Ma and
Qingsong Wen and
Xue Wang and
Liang Sun and
Rong Jin | 2,022 | null | https://proceedings.mlr.press/v162/zhou22g.html | null | null | FEDformer: Frequency Enhanced Decomposed Transformer for Long-term
Series Forecasting | MAZiqing/FEDformer | https://github.com/MAZiqing/FEDformer | Frequency Enhanced Decomposed Transformer (FEDformer) is more efficient than standard Transformer with a linear complexity to the sequence length.See more |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | MICN | \cite{MICN} | {MICN:} Multi-scale Local and Global Context Modeling for Long-term
Series Forecasting | null | null | true | false | Huiqiang Wang and
Jian Peng and
Feihu Huang and
Jince Wang and
Junhui Chen and
Yifei Xiao | 2,023 | null | https://openreview.net/forum?id=zt53IDUR1U | null | null | {MICN:} Multi-scale Local and Global Context Modeling for Long-term
Series Forecasting | Modeling Temporal Symmetry: Dual-Component Framework for ... | https://www.mdpi.com/2073-8994/17/4/577 | Micn: Multi-scale local and global context modeling for long-term series forecasting. In Proceedings of the Eleventh International Conference on Learning |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | TimesNet | \cite{TimesNet} | TimesNet: Temporal 2D-Variation Modeling for General Time Series
Analysis | http://arxiv.org/abs/2210.02186v3 | Time series analysis is of immense importance in extensive applications, such
as weather forecasting, anomaly detection, and action recognition. This paper
focuses on temporal variation modeling, which is the common key problem of
extensive analysis tasks. Previous methods attempt to accomplish this directly
from the 1... | true | true | Haixu Wu and
Tengge Hu and
Yong Liu and
Hang Zhou and
Jianmin Wang and
Mingsheng Long | 2,023 | null | https://openreview.net/forum?id=ju\_Uqw384Oq | null | null | TimesNet: Temporal 2D-Variation Modeling for General Time Series
Analysis | The complete code and scripts of TimesNet ... | https://github.com/thuml/TimesNet | GitHub - thuml/TimesNet: About Code release for "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis" (ICLR 2023), https://openreview.net/pdf?id=ju_Uqw384Oq * GitHub Advanced Security Enterprise-grade security features About Code release for "TimesNet: Temporal 2D-Variation Modeling for General ... |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | DLinear | \cite{DLinear} | Are Transformers Effective for Time Series Forecasting? | http://arxiv.org/abs/2205.13504v3 | Recently, there has been a surge of Transformer-based solutions for the
long-term time series forecasting (LTSF) task. Despite the growing performance
over the past few years, we question the validity of this line of research in
this work. Specifically, Transformers is arguably the most successful solution
to extract t... | true | true | Ailing Zeng and
Muxi Chen and
Lei Zhang and
Qiang Xu | 2,023 | null | null | 10.1609/AAAI.V37I9.26317 | null | Are Transformers Effective for Time Series Forecasting? | Are Transformers Effective for Time Series Forecasting? | http://arxiv.org/pdf/2205.13504v3 | Recently, there has been a surge of Transformer-based solutions for the
long-term time series forecasting (LTSF) task. Despite the growing performance
over the past few years, we question the validity of this line of research in
this work. Specifically, Transformers is arguably the most successful solution
to extract t... |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | TSMixer | \cite{TSMixer} | TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series
Forecasting | http://arxiv.org/abs/2306.09364v4 | Transformers have gained popularity in time series forecasting for their
ability to capture long-sequence interactions. However, their high memory and
computing requirements pose a critical bottleneck for long-term forecasting. To
address this, we propose TSMixer, a lightweight neural architecture exclusively
composed ... | true | true | Vijay Ekambaram and
Arindam Jati and
Nam Nguyen and
Phanwadee Sinthong and
Jayant Kalagnanam | 2,023 | null | null | 10.1145/3580305.3599533 | null | TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series
Forecasting | Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting | https://dl.acm.org/doi/10.1145/3580305.3599533 | TSMixer is designed for multivariate forecasting and representation learning on patched time series, providing an efficient alternative to Transformers. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | DeepAR | \cite{DeepAR} | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | http://arxiv.org/abs/1704.04110v3 | Probabilistic forecasting, i.e. estimating the probability distribution of a
time series' future given its past, is a key enabler for optimizing business
processes. In retail businesses, for example, forecasting demand is crucial for
having the right inventory available at the right time at the right place. In
this pap... | true | true | David Salinas and
Valentin Flunkert and
Jan Gasthaus and
Tim Januschowski | 2,020 | null | null | https://doi.org/10.1016/j.ijforecast.2019.07.001 | International Journal of Forecasting | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | http://arxiv.org/pdf/1704.04110v3 | Probabilistic forecasting, i.e. estimating the probability distribution of a
time series' future given its past, is a key enabler for optimizing business
processes. In retail businesses, for example, forecasting demand is crucial for
having the right inventory available at the right time at the right place. In
this pap... |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | D3VAE | \cite{D3VAE} | Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement | null | null | true | false | Yan Li and
Xinjiang Lu and
Yaqing Wang and
Dejing Dou | 2,022 | null | http://papers.nips.cc/paper\_files/paper/2022/hash/91a85f3fb8f570e6be52b333b5ab017a-Abstract-Conference.html | null | null | Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement | Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement | http://arxiv.org/pdf/2301.03028v1 | Time series forecasting has been a widely explored task of great importance
in many applications. However, it is common that real-world time series data
are recorded in a short time period, which results in a big gap between the
deep model and the limited and noisy time series. In this work, we propose to
address the t... |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | CF-RNN | \cite{CF-RNN} | Conformal Time-series Forecasting | null | null | true | false | Kamile Stankeviciute and
Ahmed M. Alaa and
Mihaela van der Schaar | 2,021 | null | https://proceedings.neurips.cc/paper/2021/hash/312f1ba2a72318edaaa995a67835fad5-Abstract.html | null | null | Conformal Time-series Forecasting | Conformal Time-Series Forecasting | https://proceedings.neurips.cc/paper/2021/file/312f1ba2a72318edaaa995a67835fad5-Paper.pdf | by K Stankeviciute · 2021 · Cited by 189 — In this work, we extend the inductive conformal prediction framework to the time-series forecasting setup, and propose a lightweight uncertainty estimation |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | EnbPI | \cite{EnbPI} | Conformal prediction interval for dynamic time-series | null | null | true | false | Chen Xu and
Yao Xie | 2,021 | null | http://proceedings.mlr.press/v139/xu21h.html | null | null | Conformal prediction interval for dynamic time-series | [PDF] Conformal Prediction Interval for Dynamic Time-Series | https://proceedings.mlr.press/v139/xu21h/xu21h.pdf | Abstract. We develop a method to construct distribution- free prediction intervals for dynamic time-series, called EnbPI that wraps around any bootstrap. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | EnbPI2 | \cite{EnbPI2} | Conformal prediction for time series | http://arxiv.org/abs/2010.09107v15 | We develop a general framework for constructing distribution-free prediction
intervals for time series. Theoretically, we establish explicit bounds on
conditional and marginal coverage gaps of estimated prediction intervals, which
asymptotically converge to zero under additional assumptions. We obtain similar
bounds on... | true | true | Chen Xu and
Yao Xie | 2,023 | null | null | 10.1109/TPAMI.2023.3272339 | {IEEE} Trans. Pattern Anal. Mach. Intell. | Conformal prediction for time series | Conformal prediction for time series | http://arxiv.org/pdf/2010.09107v15 | We develop a general framework for constructing distribution-free prediction
intervals for time series. Theoretically, we establish explicit bounds on
conditional and marginal coverage gaps of estimated prediction intervals, which
asymptotically converge to zero under additional assumptions. We obtain similar
bounds on... |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | Prescriptive | \cite{Prescriptive} | From Predictive to Prescriptive Analytics | http://arxiv.org/abs/1402.5481v4 | In this paper, we combine ideas from machine learning (ML) and operations
research and management science (OR/MS) in developing a framework, along with
specific methods, for using data to prescribe optimal decisions in OR/MS
problems. In a departure from other work on data-driven optimization and
reflecting our practic... | true | true | Dimitris Bertsimas and
Nathan Kallus | 2,020 | null | null | 10.1287/MNSC.2018.3253 | Manag. Sci. | From Predictive to Prescriptive Analytics | From Predictive to Prescriptive Analytics | http://arxiv.org/pdf/1402.5481v4 | In this paper, we combine ideas from machine learning (ML) and operations
research and management science (OR/MS) in developing a framework, along with
specific methods, for using data to prescribe optimal decisions in OR/MS
problems. In a departure from other work on data-driven optimization and
reflecting our practic... |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | PtO-bound | \cite{PtO-bound} | Generalization Bounds in the Predict-then-Optimize Framework | http://arxiv.org/abs/1905.11488v3 | The predict-then-optimize framework is fundamental in many practical
settings: predict the unknown parameters of an optimization problem, and then
solve the problem using the predicted values of the parameters. A natural loss
function in this environment is to consider the cost of the decisions induced
by the predicted... | true | true | Othman El Balghiti and
Adam N. Elmachtoub and
Paul Grigas and
Ambuj Tewari | 2,019 | null | https://proceedings.neurips.cc/paper/2019/hash/a70145bf8b173e4496b554ce57969e24-Abstract.html | null | null | Generalization Bounds in the Predict-then-Optimize Framework | [PDF] Generalization Bounds in the Predict-then-Optimize Framework | https://www.ambujtewari.com/research/elbalghiti22generalization.pdf | The predict-then-optimize framework is fundamental in many practical settings: predict the unknown param- eters of an optimization problem, and then solve |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | PTOCA | \cite{PTOCA} | A Predict-Then-Optimize Couriers Allocation Framework for Emergency Last-mile Logistics | null | null | true | false | Kaiwen Xia and
Li Lin and
Shuai Wang and
Haotian Wang and
Desheng Zhang and
Tian He | 2,023 | null | null | 10.1145/3580305.3599766 | null | A Predict-Then-Optimize Couriers Allocation Framework for Emergency Last-mile Logistics | A Predict-Then-Optimize Couriers Allocation ... - BibSonomy | https://www.bibsonomy.org/bibtex/1340e4e6e70a4e7eaefc9a9d0241a9e82 | A Predict-Then-Optimize Couriers Allocation Framework for Emergency Last-mile Logistics. K. Xia, L. Lin, S. Wang, H. Wang, D. Zhang, and T. He. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | PTOFA | \cite{PTOFA} | A Predict-Then-Optimize Customer Allocation Framework for Online Fund
Recommendation | http://arxiv.org/abs/2503.03165v1 | With the rapid growth of online investment platforms, funds can be
distributed to individual customers online. The central issue is to match funds
with potential customers under constraints. Most mainstream platforms adopt the
recommendation formulation to tackle the problem. However, the traditional
recommendation reg... | true | true | Tang, Xing and Weng, Yunpeng and Lyu, Fuyuan and Liu, Dugang and He, Xiuqiang | 2,025 | null | null | null | arXiv preprint arXiv:2503.03165 | A Predict-Then-Optimize Customer Allocation Framework for Online Fund
Recommendation | [Literature Review] A Predict-Then-Optimize Customer ... | https://www.themoonlight.io/en/review/a-predict-then-optimize-customer-allocation-framework-for-online-fund-recommendation | The paper presents a novel Predict-Then-Optimize Fund Allocation (PTOFA) framework designed to address the challenges of matching funds with |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | PTO-PNO-Benchmark | \cite{PTO-PNO-Benchmark} | Benchmarking PtO and PnO Methods in the Predictive Combinatorial
Optimization Regime | http://arxiv.org/abs/2311.07633v5 | Predictive combinatorial optimization, where the parameters of combinatorial
optimization (CO) are unknown at the decision-making time, is the precise
modeling of many real-world applications, including energy cost-aware
scheduling and budget allocation on advertising. Tackling such a problem
usually involves a predict... | true | true | Geng, Haoyu and
Ruan, Hang and
Wang, Runzhong and
Li, Yang and
Wang, Yang and
Chen, Lei and
Yan, Junchi | 2,024 | null | https://openreview.net/forum?id=cX57Pbw8vS | null | null | Benchmarking PtO and PnO Methods in the Predictive Combinatorial
Optimization Regime | Benchmarking PtO and PnO Methods in the Predictive ... | https://arxiv.org/html/2311.07633v5 | Predictive combinatorial optimization is a family of Combinatorial Optimization (CO) problems where the problem parameters are unknown during decision-making. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | PtOorPnO | \cite{PtOorPnO} | Predict-then-optimize or predict-and-optimize? An empirical evaluation
of cost-sensitive learning strategies | null | null | true | false | Toon Vanderschueren and
Tim Verdonck and
Bart Baesens and
Wouter Verbeke | 2,022 | null | null | 10.1016/J.INS.2022.02.021 | Inf. Sci. | Predict-then-optimize or predict-and-optimize? An empirical evaluation
of cost-sensitive learning strategies | Predict-then-optimize or predict-and-optimize? An ... | https://repository.uantwerpen.be/link/irua/189575 | by T Vanderschueren · 2022 · Cited by 81 — Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies. Author. Vanderschueren, Toon. Verdonck, Tim. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | PnO-bound | \cite{PnO-bound} | Risk Bounds and Calibration for a Smart Predict-then-Optimize Method | http://arxiv.org/abs/2108.08887v2 | The predict-then-optimize framework is fundamental in practical stochastic
decision-making problems: first predict unknown parameters of an optimization
model, then solve the problem using the predicted values. A natural loss
function in this setting is defined by measuring the decision error induced by
the predicted p... | true | true | Heyuan Liu and
Paul Grigas | 2,021 | null | https://proceedings.neurips.cc/paper/2021/hash/b943325cc7b7422d2871b345bf9b067f-Abstract.html | null | null | Risk Bounds and Calibration for a Smart Predict-then-Optimize Method | [PDF] Risk Bounds and Calibration for a Smart Predict-then-Optimize Method | https://papers.neurips.cc/paper/2021/file/b943325cc7b7422d2871b345bf9b067f-Paper.pdf | The predict-then-optimize framework is fundamental in practical stochastic decision-making problems: first predict unknown parameters of an optimization. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | DFL-Survey | \cite{DFL-Survey} | Decision-Focused Learning: Foundations, State of the Art, Benchmark and
Future Opportunities | http://arxiv.org/abs/2307.13565v4 | Decision-focused learning (DFL) is an emerging paradigm that integrates
machine learning (ML) and constrained optimization to enhance decision quality
by training ML models in an end-to-end system. This approach shows significant
potential to revolutionize combinatorial decision-making in real-world
applications that o... | true | true | Jayanta Mandi and
James Kotary and
Senne Berden and
Maxime Mulamba and
Victor Bucarey and
Tias Guns and
Ferdinando Fioretto | 2,024 | null | null | 10.1613/JAIR.1.15320 | J. Artif. Intell. Res. | Decision-Focused Learning: Foundations, State of the Art, Benchmark and
Future Opportunities | View of Decision-Focused Learning: Foundations, State ... | https://jair.org/index.php/jair/article/view/15320/27076 | by J Mandi · 2024 · Cited by 107 — Return to Article Details Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities Download Download PDF. Thumbnails |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | OptNet | \cite{OptNet} | OptNet: Differentiable Optimization as a Layer in Neural Networks | http://arxiv.org/abs/1703.00443v5 | This paper presents OptNet, a network architecture that integrates
optimization problems (here, specifically in the form of quadratic programs) as
individual layers in larger end-to-end trainable deep networks. These layers
encode constraints and complex dependencies between the hidden states that
traditional convoluti... | true | true | Brandon Amos and
J. Zico Kolter | 2,017 | null | http://proceedings.mlr.press/v70/amos17a.html | null | null | OptNet: Differentiable Optimization as a Layer in Neural Networks | OptNet: Differentiable Optimization as a Layer in Neural Networks | http://arxiv.org/pdf/1703.00443v5 | This paper presents OptNet, a network architecture that integrates
optimization problems (here, specifically in the form of quadratic programs) as
individual layers in larger end-to-end trainable deep networks. These layers
encode constraints and complex dependencies between the hidden states that
traditional convoluti... |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | Cvxpylayers | \cite{Cvxpylayers} | Differentiable Convex Optimization Layers | http://arxiv.org/abs/1910.12430v1 | Recent work has shown how to embed differentiable optimization problems (that
is, problems whose solutions can be backpropagated through) as layers within
deep learning architectures. This method provides a useful inductive bias for
certain problems, but existing software for differentiable optimization layers
is rigid... | true | true | Akshay Agrawal and
Brandon Amos and
Shane T. Barratt and
Stephen P. Boyd and
Steven Diamond and
J. Zico Kolter | 2,019 | null | https://proceedings.neurips.cc/paper/2019/hash/9ce3c52fc54362e22053399d3181c638-Abstract.html | null | null | Differentiable Convex Optimization Layers | Differentiable Convex Optimization Layers | http://arxiv.org/pdf/1910.12430v1 | Recent work has shown how to embed differentiable optimization problems (that
is, problems whose solutions can be backpropagated through) as layers within
deep learning architectures. This method provides a useful inductive bias for
certain problems, but existing software for differentiable optimization layers
is rigid... |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | NCE | \cite{NCE} | Contrastive Losses and Solution Caching for Predict-and-Optimize | http://arxiv.org/abs/2011.05354v2 | Many decision-making processes involve solving a combinatorial optimization
problem with uncertain input that can be estimated from historic data.
Recently, problems in this class have been successfully addressed via
end-to-end learning approaches, which rely on solving one optimization problem
for each training instan... | true | true | Maxime Mulamba and
Jayanta Mandi and
Michelangelo Diligenti and
Michele Lombardi and
Victor Bucarey and
Tias Guns | 2,021 | null | null | 10.24963/IJCAI.2021/390 | null | Contrastive Losses and Solution Caching for Predict-and-Optimize | [PDF] Contrastive Losses and Solution Caching for Predict-and-Optimize | https://people.cs.kuleuven.be/~tias.guns/files/ijcai21_nce_solpool.pdf | In contrast, our con- trastive losses, coupled with a solution caching mechanism, do away with repeatedly solving the optimization problem. |
Timing is important: Risk-aware Fund Allocation based on Time-Series
Forecasting | 2505.24835v1 | SPO+ | \cite{SPO+} | Smart "Predict, then Optimize" | http://arxiv.org/abs/1710.08005v5 | Many real-world analytics problems involve two significant challenges:
prediction and optimization. Due to the typically complex nature of each
challenge, the standard paradigm is predict-then-optimize. By and large,
machine learning tools are intended to minimize prediction error and do not
account for how the predict... | true | true | Adam N. Elmachtoub and
Paul Grigas | 2,022 | null | null | 10.1287/MNSC.2020.3922 | Manag. Sci. | Smart "Predict, then Optimize" | Smart "Predict, then Optimize" | http://arxiv.org/pdf/1710.08005v5 | Many real-world analytics problems involve two significant challenges:
prediction and optimization. Due to the typically complex nature of each
challenge, the standard paradigm is predict-then-optimize. By and large,
machine learning tools are intended to minimize prediction error and do not
account for how the predict... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | jumper2021highly | \cite{jumper2021highly} | Highly accurate protein structure prediction with AlphaFold | null | null | true | false | Jumper, John and Evans, Richard and Pritzel, Alexander and Green, Tim and Figurnov, Michael and Ronneberger, Olaf and Tunyasuvunakool, Kathryn and Bates, Russ and {\v{Z}}{\'\i}dek, Augustin and Potapenko, Anna and others | 2,021 | null | null | null | Nature | Highly accurate protein structure prediction with AlphaFold | Highly accurate protein structure prediction with AlphaFold - Nature | https://www.nature.com/articles/s41586-021-03819-2 | Highly accurate protein structure prediction with AlphaFold | Nature We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15."), demonstrating accuracy competitive with experimental structures in a m... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | noe2019boltzmann | \cite{noe2019boltzmann} | Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems
with Deep Learning | http://arxiv.org/abs/1812.01729v2 | Computing equilibrium states in condensed-matter many-body systems, such as
solvated proteins, is a long-standing challenge. Lacking methods for generating
statistically independent equilibrium samples in "one shot", vast computational
effort is invested for simulating these system in small steps, e.g., using
Molecular... | true | true | No{\'e}, Frank and Olsson, Simon and K{\"o}hler, Jonas and Wu, Hao | 2,019 | null | null | null | Science | Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems
with Deep Learning | (PDF) Boltzmann generators: Sampling equilibrium states ... | https://www.researchgate.net/publication/335645955_Boltzmann_generators_Sampling_equilibrium_states_of_many-body_systems_with_deep_learning | Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning. September 2019; Science 365(6457):eaaw1147. DOI |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | arts2023two | \cite{arts2023two} | Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics | null | null | true | false | Arts, Marloes and Satorras, Victor Garcia and Huang, Chin-Wei and Zuegner, Daniel and Federici, Marco and Clementi, Cecilia and No{\'e}, Frank and Pinsler, Robert and Berg, Rianne van den | 2,023 | null | null | null | arXiv preprint arXiv:2302.00600 | Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics | Two for One: Diffusion Models and Force Fields for Coarse-Grained ... | https://arxiv.org/abs/2302.00600 | In this work, we leverage connections between score-based generative models, force fields and molecular dynamics to learn a CG force field |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | jing2023eigenfold | \cite{jing2023eigenfold} | EigenFold: Generative Protein Structure Prediction with Diffusion Models | http://arxiv.org/abs/2304.02198v1 | Protein structure prediction has reached revolutionary levels of accuracy on
single structures, yet distributional modeling paradigms are needed to capture
the conformational ensembles and flexibility that underlie biological function.
Towards this goal, we develop EigenFold, a diffusion generative modeling
framework f... | true | true | Jing, Bowen and Erives, Ezra and Pao-Huang, Peter and Corso, Gabriele and Berger, Bonnie and Jaakkola, Tommi | 2,023 | null | null | null | arXiv preprint arXiv:2304.02198 | EigenFold: Generative Protein Structure Prediction with Diffusion Models | EigenFold: Generative Protein Structure Prediction with Diffusion Models | http://arxiv.org/pdf/2304.02198v1 | Protein structure prediction has reached revolutionary levels of accuracy on
single structures, yet distributional modeling paradigms are needed to capture
the conformational ensembles and flexibility that underlie biological function.
Towards this goal, we develop EigenFold, a diffusion generative modeling
framework f... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | lu2024str2str | \cite{lu2024str2str} | Str2Str: A Score-based Framework for Zero-shot Protein Conformation
Sampling | http://arxiv.org/abs/2306.03117v3 | The dynamic nature of proteins is crucial for determining their biological
functions and properties, for which Monte Carlo (MC) and molecular dynamics
(MD) simulations stand as predominant tools to study such phenomena. By
utilizing empirically derived force fields, MC or MD simulations explore the
conformational space... | true | true | Lu, Jiarui and Zhong, Bozitao and Zhang, Zuobai and Tang, Jian | 2,024 | null | null | null | null | Str2Str: A Score-based Framework for Zero-shot Protein Conformation
Sampling | Codebase of the paper "Str2Str: A Score-based Framework for Zero ... | https://github.com/lujiarui/Str2Str | Str2Str is a score-based framework (which means it can accommodate any diffusion/flow matching architecture) for protein conformation sampling in a zero-shot |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | zheng2024predicting | \cite{zheng2024predicting} | Towards Predicting Equilibrium Distributions for Molecular Systems with
Deep Learning | http://arxiv.org/abs/2306.05445v1 | Advances in deep learning have greatly improved structure prediction of
molecules. However, many macroscopic observations that are important for
real-world applications are not functions of a single molecular structure, but
rather determined from the equilibrium distribution of structures. Traditional
methods for obtai... | true | true | Zheng, Shuxin and He, Jiyan and Liu, Chang and Shi, Yu and Lu, Ziheng and Feng, Weitao and Ju, Fusong and Wang, Jiaxi and Zhu, Jianwei and Min, Yaosen and others | 2,024 | null | null | null | Nature Machine Intelligence | Towards Predicting Equilibrium Distributions for Molecular Systems with
Deep Learning | Towards Predicting Equilibrium Distributions for Molecular Systems ... | https://arxiv.org/abs/2306.05445 | In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | wang2024proteinconformationgenerationforceguided | \cite{wang2024proteinconformationgenerationforceguided} | Protein Conformation Generation via Force-Guided SE(3) Diffusion Models | http://arxiv.org/abs/2403.14088v2 | The conformational landscape of proteins is crucial to understanding their
functionality in complex biological processes. Traditional physics-based
computational methods, such as molecular dynamics (MD) simulations, suffer from
rare event sampling and long equilibration time problems, hindering their
applications in ge... | true | true | Yan Wang and Lihao Wang and Yuning Shen and Yiqun Wang and Huizhuo Yuan and Yue Wu and Quanquan Gu | 2,024 | null | https://arxiv.org/abs/2403.14088 | null | null | Protein Conformation Generation via Force-Guided SE(3) Diffusion Models | Official Implemetation of ConfDiff (ICML'24) - Protein Conformation ... | https://github.com/bytedance/ConfDiff | A force-guided SE(3) diffusion model for protein conformation generation. ConfDiff can generate protein conformations with rich diversity while preserving high |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | jing2024alphafoldmeetsflowmatching | \cite{jing2024alphafoldmeetsflowmatching} | AlphaFold Meets Flow Matching for Generating Protein Ensembles | http://arxiv.org/abs/2402.04845v2 | The biological functions of proteins often depend on dynamic structural
ensembles. In this work, we develop a flow-based generative modeling approach
for learning and sampling the conformational landscapes of proteins. We
repurpose highly accurate single-state predictors such as AlphaFold and ESMFold
and fine-tune them... | true | true | Bowen Jing and Bonnie Berger and Tommi Jaakkola | 2,024 | null | https://arxiv.org/abs/2402.04845 | null | null | AlphaFold Meets Flow Matching for Generating Protein Ensembles | AlphaFold Meets Flow Matching for Generating Protein Ensembles | http://arxiv.org/pdf/2402.04845v2 | The biological functions of proteins often depend on dynamic structural
ensembles. In this work, we develop a flow-based generative modeling approach
for learning and sampling the conformational landscapes of proteins. We
repurpose highly accurate single-state predictors such as AlphaFold and ESMFold
and fine-tune them... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | lu2024structure | \cite{lu2024structure} | Structure Language Models for Protein Conformation Generation | http://arxiv.org/abs/2410.18403v2 | Proteins adopt multiple structural conformations to perform their diverse
biological functions, and understanding these conformations is crucial for
advancing drug discovery. Traditional physics-based simulation methods often
struggle with sampling equilibrium conformations and are computationally
expensive. Recently, ... | true | true | Lu, Jiarui and Chen, Xiaoyin and Lu, Stephen Zhewen and Shi, Chence and Guo, Hongyu and Bengio, Yoshua and Tang, Jian | 2,024 | null | null | null | arXiv preprint arXiv:2410.18403 | Structure Language Models for Protein Conformation Generation | Structure Language Models for Protein Conformation Generation | http://arxiv.org/pdf/2410.18403v2 | Proteins adopt multiple structural conformations to perform their diverse
biological functions, and understanding these conformations is crucial for
advancing drug discovery. Traditional physics-based simulation methods often
struggle with sampling equilibrium conformations and are computationally
expensive. Recently, ... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | jing2024generative | \cite{jing2024generative} | Generative Modeling of Molecular Dynamics Trajectories | http://arxiv.org/abs/2409.17808v1 | Molecular dynamics (MD) is a powerful technique for studying microscopic
phenomena, but its computational cost has driven significant interest in the
development of deep learning-based surrogate models. We introduce generative
modeling of molecular trajectories as a paradigm for learning flexible
multi-task surrogate m... | true | true | Jing, Bowen and St{\"a}rk, Hannes and Jaakkola, Tommi and Berger, Bonnie | 2,024 | null | null | null | arXiv preprint arXiv:2409.17808 | Generative Modeling of Molecular Dynamics Trajectories | Generative Modeling of Molecular Dynamics Trajectories | http://arxiv.org/pdf/2409.17808v1 | Molecular dynamics (MD) is a powerful technique for studying microscopic
phenomena, but its computational cost has driven significant interest in the
development of deep learning-based surrogate models. We introduce generative
modeling of molecular trajectories as a paradigm for learning flexible
multi-task surrogate m... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | kreutzer2018reliability | \cite{kreutzer2018reliability} | Reliability and Learnability of Human Bandit Feedback for
Sequence-to-Sequence Reinforcement Learning | http://arxiv.org/abs/1805.10627v3 | We present a study on reinforcement learning (RL) from human bandit feedback
for sequence-to-sequence learning, exemplified by the task of bandit neural
machine translation (NMT). We investigate the reliability of human bandit
feedback, and analyze the influence of reliability on the learnability of a
reward estimator,... | true | true | Julia Kreutzer and Joshua Uyheng and S. Riezler | 2,018 | null | null | 10.18653/v1/P18-1165 | Annual Meeting of the Association for Computational Linguistics | Reliability and Learnability of Human Bandit Feedback for
Sequence-to-Sequence Reinforcement Learning | Reliability and Learnability of Human Bandit Feedback for ... | https://aclanthology.org/P18-1165/ | Missing: 04/08/2025 |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | stiennon2020learning | \cite{stiennon2020learning} | Learning to summarize from human feedback | http://arxiv.org/abs/2009.01325v3 | As language models become more powerful, training and evaluation are
increasingly bottlenecked by the data and metrics used for a particular task.
For example, summarization models are often trained to predict human reference
summaries and evaluated using ROUGE, but both of these metrics are rough
proxies for what we r... | true | true | Nisan Stiennon and Long Ouyang and Jeff Wu and Daniel M. Ziegler and Ryan J. Lowe and Chelsea Voss and Alec Radford and Dario Amodei and Paul Christiano | 2,020 | null | null | null | Neural Information Processing Systems | Learning to summarize from human feedback | Learning to summarize from human feedback | http://arxiv.org/pdf/2009.01325v3 | As language models become more powerful, training and evaluation are
increasingly bottlenecked by the data and metrics used for a particular task.
For example, summarization models are often trained to predict human reference
summaries and evaluated using ROUGE, but both of these metrics are rough
proxies for what we r... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | ouyang2022training | \cite{ouyang2022training} | Training language models to follow instructions with human feedback | null | null | true | false | Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and John Schulman and Jacob Hilton and Fraser Kelton and Luke E. Miller and Maddie Simens and Amanda Askell and P. Welinder and P. Christiano and J. Le... | 2,022 | null | null | null | Neural Information Processing Systems | Training language models to follow instructions with human feedback | Training language models to follow instructions with human feedback | http://arxiv.org/pdf/2203.02155v1 | Making language models bigger does not inherently make them better at
following a user's intent. For example, large language models can generate
outputs that are untruthful, toxic, or simply not helpful to the user. In other
words, these models are not aligned with their users. In this paper, we show an
avenue for alig... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | black2023training | \cite{black2023training} | Training Diffusion Models with Reinforcement Learning | http://arxiv.org/abs/2305.13301v4 | Diffusion models are a class of flexible generative models trained with an
approximation to the log-likelihood objective. However, most use cases of
diffusion models are not concerned with likelihoods, but instead with
downstream objectives such as human-perceived image quality or drug
effectiveness. In this paper, we ... | true | true | Black, Kevin and Janner, Michael and Du, Yilun and Kostrikov, Ilya and Levine, Sergey | 2,023 | null | null | null | arXiv preprint arXiv:2305.13301 | Training Diffusion Models with Reinforcement Learning | Training Diffusion Models with Reinforcement Learning | http://arxiv.org/pdf/2305.13301v4 | Diffusion models are a class of flexible generative models trained with an
approximation to the log-likelihood objective. However, most use cases of
diffusion models are not concerned with likelihoods, but instead with
downstream objectives such as human-perceived image quality or drug
effectiveness. In this paper, we ... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | fan2024reinforcement | \cite{fan2024reinforcement} | Reinforcement learning for fine-tuning text-to-image diffusion models | null | null | true | false | Fan, Ying and Watkins, Olivia and Du, Yuqing and Liu, Hao and Ryu, Moonkyung and Boutilier, Craig and Abbeel, Pieter and Ghavamzadeh, Mohammad and Lee, Kangwook and Lee, Kimin | 2,024 | null | null | null | Advances in Neural Information Processing Systems | Reinforcement learning for fine-tuning text-to-image diffusion models | DPOK: Reinforcement Learning for Fine-tuning Text-to-Image ... - arXiv | https://arxiv.org/abs/2305.16381 | We propose using online reinforcement learning (RL) to fine-tune text-to-image models. We focus on diffusion models, defining the fine-tuning task as an RL |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | rafailov2024direct | \cite{rafailov2024direct} | Direct Preference Optimization: Your Language Model is Secretly a Reward
Model | http://arxiv.org/abs/2305.18290v3 | While large-scale unsupervised language models (LMs) learn broad world
knowledge and some reasoning skills, achieving precise control of their
behavior is difficult due to the completely unsupervised nature of their
training. Existing methods for gaining such steerability collect human labels
of the relative quality of... | true | true | Rafailov, Rafael and Sharma, Archit and Mitchell, Eric and Manning, Christopher D and Ermon, Stefano and Finn, Chelsea | 2,024 | null | null | null | Advances in Neural Information Processing Systems | Direct Preference Optimization: Your Language Model is Secretly a Reward
Model | Direct Preference Optimization: Your Language Model is Secretly a ... | https://arxiv.org/abs/2305.18290 | **arXiv:2305.18290** (cs) View a PDF of the paper titled Direct Preference Optimization: Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other authors View a PDF of the paper titled Direct Preference Optimization: Your Language Model is Secretly a Reward Model, by Rafael Rafailov and 5 other a... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | Wallace_2024_CVPR | \cite{Wallace_2024_CVPR} | Diffusion Model Alignment Using Direct Preference Optimization | http://arxiv.org/abs/2311.12908v1 | Large language models (LLMs) are fine-tuned using human comparison data with
Reinforcement Learning from Human Feedback (RLHF) methods to make them better
aligned with users' preferences. In contrast to LLMs, human preference learning
has not been widely explored in text-to-image diffusion models; the best
existing app... | true | true | Wallace, Bram and Dang, Meihua and Rafailov, Rafael and Zhou, Linqi and Lou, Aaron and Purushwalkam, Senthil and Ermon, Stefano and Xiong, Caiming and Joty, Shafiq and Naik, Nikhil | 2,024 | June | null | null | null | Diffusion Model Alignment Using Direct Preference Optimization | Diffusion Model Alignment Using Direct Preference Optimization | http://arxiv.org/pdf/2311.12908v1 | Large language models (LLMs) are fine-tuned using human comparison data with
Reinforcement Learning from Human Feedback (RLHF) methods to make them better
aligned with users' preferences. In contrast to LLMs, human preference learning
has not been widely explored in text-to-image diffusion models; the best
existing app... |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | zhou2024antigen | \cite{zhou2024antigen} | Antigen-Specific Antibody Design via Direct Energy-based Preference
Optimization | http://arxiv.org/abs/2403.16576v3 | Antibody design, a crucial task with significant implications across various
disciplines such as therapeutics and biology, presents considerable challenges
due to its intricate nature. In this paper, we tackle antigen-specific antibody
sequence-structure co-design as an optimization problem towards specific
preferences... | true | true | Zhou, Xiangxin and Xue, Dongyu and Chen, Ruizhe and Zheng, Zaixiang and Wang, Liang and Gu, Quanquan | 2,024 | null | null | null | arXiv preprint arXiv:2403.16576 | Antigen-Specific Antibody Design via Direct Energy-based Preference
Optimization | Antigen-Specific Antibody Design via Direct Energy-based ... | https://openreview.net/forum?id=GN2GXjPyN8&referrer=%5Bthe%20profile%20of%20Xiangxin%20Zhou%5D(%2Fprofile%3Fid%3D~Xiangxin_Zhou1) | Summary: This paper applies direct preference optimization to antibody design. Specifically, it uses Rosetta binding energy to guide a pre-trained diffusion |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | alford2017rosetta | \cite{alford2017rosetta} | The Rosetta all-atom energy function for macromolecular modeling and design | null | null | true | false | Alford, Rebecca F and Leaver-Fay, Andrew and Jeliazkov, Jeliazko R and O’Meara, Matthew J and DiMaio, Frank P and Park, Hahnbeom and Shapovalov, Maxim V and Renfrew, P Douglas and Mulligan, Vikram K and Kappel, Kalli and others | 2,017 | null | null | null | Journal of chemical theory and computation | The Rosetta all-atom energy function for macromolecular modeling and design | The Rosetta all-atom energy function for macromolecular ... | https://pmc.ncbi.nlm.nih.gov/articles/PMC5717763/ | by RF Alford · 2017 · Cited by 1630 — The goal of this paper is to describe the energy calculations used by the Rosetta macromolecular modeling program: we explain the underlying physical concepts, |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | gu2024aligning | \cite{gu2024aligning} | Aligning Target-Aware Molecule Diffusion Models with Exact Energy
Optimization | http://arxiv.org/abs/2407.01648v2 | Generating ligand molecules for specific protein targets, known as
structure-based drug design, is a fundamental problem in therapeutics
development and biological discovery. Recently, target-aware generative models,
especially diffusion models, have shown great promise in modeling
protein-ligand interactions and gener... | true | true | Gu, Siyi and Xu, Minkai and Powers, Alexander and Nie, Weili and Geffner, Tomas and Kreis, Karsten and Leskovec, Jure and Vahdat, Arash and Ermon, Stefano | 2,024 | null | null | null | arXiv preprint arXiv:2407.01648 | Aligning Target-Aware Molecule Diffusion Models with Exact Energy
Optimization | [PDF] Aligning Target-Aware Molecule Diffusion Models with Exact Energy ... | https://proceedings.neurips.cc/paper_files/paper/2024/file/4ddfe69f164eae70abc86f0f9cbed7e8-Paper-Conference.pdf | ALIDIFF aligns target-aware diffusion models with preferred properties, shifting chemical distribution towards higher binding affinity and structural |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | 2505.24203v1 | cheng2024decomposed | \cite{cheng2024decomposed} | Decomposed Direct Preference Optimization for Structure-Based Drug
Design | http://arxiv.org/abs/2407.13981v2 | Diffusion models have achieved promising results for Structure-Based Drug
Design (SBDD). Nevertheless, high-quality protein subpocket and ligand data are
relatively scarce, which hinders the models' generation capabilities. Recently,
Direct Preference Optimization (DPO) has emerged as a pivotal tool for aligning
genera... | true | true | Cheng, Xiwei and Zhou, Xiangxin and Yang, Yuwei and Bao, Yu and Gu, Quanquan | 2,024 | null | null | null | arXiv preprint arXiv:2407.13981 | Decomposed Direct Preference Optimization for Structure-Based Drug
Design | Decomposed Direct Preference Optimization for Structure ... | https://arxiv.org/abs/2407.13981 | Image 2: arxiv logo>q-bio> arXiv:2407.13981 **arXiv:2407.13981** (q-bio) View a PDF of the paper titled Decomposed Direct Preference Optimization for Structure-Based Drug Design, by Xiwei Cheng and 4 other authors (or arXiv:2407.13981v2 [q-bio.BM] for this version) View a PDF of the paper titled Decomposed Direct Pre... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | GPT4 | \cite{GPT4} | {GPT-4} Technical Report | null | null | true | false | OpenAI | 2,023 | null | https://doi.org/10.48550/arXiv.2303.08774 | 10.48550/ARXIV.2303.08774 | CoRR | {GPT-4} Technical Report | (PDF) GPT-4 Technical Report - ResearchGate | https://www.researchgate.net/publication/383739523_GPT-4_Technical_Report | We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | PaLM | \cite{PaLM} | PaLM: Scaling Language Modeling with Pathways | http://arxiv.org/abs/2204.02311v5 | Large language models have been shown to achieve remarkable performance
across a variety of natural language tasks using few-shot learning, which
drastically reduces the number of task-specific training examples needed to
adapt the model to a particular application. To further our understanding of
the impact of scale o... | true | true | Aakanksha Chowdhery and
Sharan Narang and
Jacob Devlin and
Maarten Bosma and
Gaurav Mishra and
Adam Roberts and
Paul Barham and
Hyung Won Chung and
Charles Sutton and
... | 2,023 | null | http://jmlr.org/papers/v24/22-1144.html | null | J. Mach. Learn. Res. | PaLM: Scaling Language Modeling with Pathways | PaLM: Scaling Language Modeling with Pathways | http://arxiv.org/pdf/2204.02311v5 | Large language models have been shown to achieve remarkable performance
across a variety of natural language tasks using few-shot learning, which
drastically reduces the number of task-specific training examples needed to
adapt the model to a particular application. To further our understanding of
the impact of scale o... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | llama | \cite{llama} | LLaMA: Open and Efficient Foundation Language Models | http://arxiv.org/abs/2302.13971v1 | We introduce LLaMA, a collection of foundation language models ranging from
7B to 65B parameters. We train our models on trillions of tokens, and show that
it is possible to train state-of-the-art models using publicly available
datasets exclusively, without resorting to proprietary and inaccessible
datasets. In partic... | true | true | Hugo Touvron and
Thibaut Lavril and
Gautier Izacard and
Xavier Martinet and
Marie{-}Anne Lachaux and
Timoth{\'{e}}e Lacroix and
Baptiste Rozi{\`{e}}re and
Naman Goyal and
Eric ... | 2,023 | null | https://doi.org/10.48550/arXiv.2302.13971 | 10.48550/ARXIV.2302.13971 | CoRR | LLaMA: Open and Efficient Foundation Language Models | LLaMA: Open and Efficient Foundation Language Models | http://arxiv.org/pdf/2302.13971v1 | We introduce LLaMA, a collection of foundation language models ranging from
7B to 65B parameters. We train our models on trillions of tokens, and show that
it is possible to train state-of-the-art models using publicly available
datasets exclusively, without resorting to proprietary and inaccessible
datasets. In partic... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | Llama_2 | \cite{Llama_2} | Llama 2: Open Foundation and Fine-Tuned Chat Models | http://arxiv.org/abs/2307.09288v2 | In this work, we develop and release Llama 2, a collection of pretrained and
fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70
billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for
dialogue use cases. Our models outperform open-source chat models on most
benchmarks ... | true | true | Hugo Touvron and
Louis Martin and
Kevin Stone and
Peter Albert and
Amjad Almahairi and
Yasmine Babaei and
Nikolay Bashlykov and
Soumya Batra and
Prajjwal Bhargava and
... | 2,023 | null | https://doi.org/10.48550/arXiv.2307.09288 | 10.48550/ARXIV.2307.09288 | CoRR | Llama 2: Open Foundation and Fine-Tuned Chat Models | Llama 2: Open Foundation and Fine-Tuned Chat Models - Meta AI | https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/ | We develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | anthropic_claude | \cite{anthropic_claude} | Claude: A Family of AI Models | null | null | true | false | Anthropic | 2,024 | null | https://www.anthropic.com/product | null | null | Claude: A Family of AI Models | Introducing the next generation of Claude - Anthropic | https://www.anthropic.com/news/claude-3-family | The family includes three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. ### Claude 3 model family The Claude 3 models can power live customer chats, auto-completions, and data extraction tasks where responses must be immediate and in real-time. We’ve devel... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | gemma | \cite{gemma} | Gemma: Open Models Based on Gemini Research and Technology | http://arxiv.org/abs/2403.08295v4 | This work introduces Gemma, a family of lightweight, state-of-the art open
models built from the research and technology used to create Gemini models.
Gemma models demonstrate strong performance across academic benchmarks for
language understanding, reasoning, and safety. We release two sizes of models
(2 billion and 7... | true | true | Thomas Mesnard and
Cassidy Hardin and
Robert Dadashi and
Surya Bhupatiraju and
Shreya Pathak and
Laurent Sifre and
Morgane Rivi{\`{e}}re and
Mihir Sanjay Kale and
Juliette Love... | 2,024 | null | https://doi.org/10.48550/arXiv.2403.08295 | 10.48550/ARXIV.2403.08295 | CoRR | Gemma: Open Models Based on Gemini Research and Technology | Gemma: Open Models Based on Gemini Research and Technology | http://arxiv.org/pdf/2403.08295v4 | This work introduces Gemma, a family of lightweight, state-of-the art open
models built from the research and technology used to create Gemini models.
Gemma models demonstrate strong performance across academic benchmarks for
language understanding, reasoning, and safety. We release two sizes of models
(2 billion and 7... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | AIIndex2024 | \cite{AIIndex2024} | AI Index Report | null | null | true | false | stanford | null | null | null | null | null | AI Index Report | AI Index 2025: State of AI in 10 Charts | Stanford HAI | https://hai.stanford.edu/news/ai-index-2025-state-of-ai-in-10-charts | * AI Index * AI Index Report The 2025 AI Index Report, published on April 7, 2025, is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. Each year, the ... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | apple_ai | \cite{apple_ai} | Apple Intelligence | null | null | true | false | Apple | 2,024 | null | null | null | null | Apple Intelligence | How to get Apple Intelligence | https://support.apple.com/en-us/121115 | * Apple * Apple Watch * Explore All Apple Watch * Why Apple Watch * Shop Apple Watch * Apple Watch Support * Shop Apple Vision Pro * Apple Vision Pro Support * Apple TV Support * Apple One * Apple TV+ Support * Apple Watch * Made by Apple Apple Intelligence is available in beta starting with i... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | song2024powerinfer | \cite{song2024powerinfer} | PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU | http://arxiv.org/abs/2312.12456v2 | This paper introduces PowerInfer, a high-speed Large Language Model (LLM)
inference engine on a personal computer (PC) equipped with a single
consumer-grade GPU. The key principle underlying the design of PowerInfer is
exploiting the high locality inherent in LLM inference, characterized by a
power-law distribution in ... | true | true | Song, Yixin and Mi, Zeyu and Xie, Haotong and Chen, Haibo | 2,024 | null | null | null | null | PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU | Fast Large Language Model Serving with a Consumer-grade GPU | https://arxiv.org/abs/2312.12456 | This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | yuan2023mobile | \cite{yuan2023mobile} | Mobile Foundation Model as Firmware | http://arxiv.org/abs/2308.14363v3 | In today's landscape, smartphones have evolved into hubs for hosting a
multitude of deep learning models aimed at local execution. A key realization
driving this work is the notable fragmentation among these models,
characterized by varied architectures, operators, and implementations. This
fragmentation imposes a sign... | true | true | Yuan, Jinliang and Yang, Chen and Cai, Dongqi and Wang, Shihe and Yuan, Xin and Zhang, Zeling and Li, Xiang and Zhang, Dingge and Mei, Hanzi and Jia, Xianqing and others | 2,023 | null | null | null | arXiv preprint arXiv:2308.14363 | Mobile Foundation Model as Firmware | Mobile Foundation Model as Firmware | http://arxiv.org/pdf/2308.14363v3 | In today's landscape, smartphones have evolved into hubs for hosting a
multitude of deep learning models aimed at local execution. A key realization
driving this work is the notable fragmentation among these models,
characterized by varied architectures, operators, and implementations. This
fragmentation imposes a sign... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | app_3 | \cite{app_3} | Drive Like a Human: Rethinking Autonomous Driving with Large Language
Models | http://arxiv.org/abs/2307.07162v1 | In this paper, we explore the potential of using a large language model (LLM)
to understand the driving environment in a human-like manner and analyze its
ability to reason, interpret, and memorize when facing complex scenarios. We
argue that traditional optimization-based and modular autonomous driving (AD)
systems fa... | true | true | Fu, Daocheng and Li, Xin and Wen, Licheng and Dou, Min and Cai, Pinlong and Shi, Botian and Qiao, Yu | 2,024 | null | null | null | null | Drive Like a Human: Rethinking Autonomous Driving with Large Language
Models | Drive Like a Human: Rethinking Autonomous Driving with ... | https://www.computer.org/csdl/proceedings-article/wacvw/2024/702800a910/1WbOYt2kbVC | by D Fu · 2024 · Cited by 238 — In this paper, we explore the potential of using a large language model (LLM) to understand the driving environment in a human-like manner. |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | app5_robot | \cite{app5_robot} | BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous
Driving | http://arxiv.org/abs/2312.06371v2 | The ability to accurately predict the trajectory of surrounding vehicles is a
critical hurdle to overcome on the journey to fully autonomous vehicles. To
address this challenge, we pioneer a novel behavior-aware trajectory prediction
model (BAT) that incorporates insights and findings from traffic psychology,
human beh... | true | true | Haicheng Liao and
Zhenning Li and
Huanming Shen and
Wenxuan Zeng and
Dongping Liao and
Guofa Li and
Shengbo Eben Li and
Chengzhong Xu | 2,023 | null | https://doi.org/10.48550/arXiv.2312.06371 | 10.48550/ARXIV.2312.06371 | CoRR | BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous
Driving | Behavior-Aware Human-Like Trajectory Prediction for Autonomous ... | https://github.com/Petrichor625/BATraj-Behavior-aware-Model | Introducing a real-time dynamic geometric graph method for the continuous representation of driving behavior in trajectory prediction for autonomous driving. |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | app6_laptops | \cite{app6_laptops} | Creating Large Language Models on Your Laptop | null | null | true | false | Xinyu Ye and Zhe Wang and Haihao Shen and Yu Luo and Hanwen Chang | 2,023 | null | null | null | null | Creating Large Language Models on Your Laptop | How to run an LLM on your laptop | https://www.technologyreview.com/2025/07/17/1120391/how-to-run-an-llm-on-your-laptop/ | It's now possible to run useful models from the safety and comfort of your own computer. Here's how. |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | ExeGPT | \cite{ExeGPT} | ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference | http://arxiv.org/abs/2404.07947v1 | This paper presents ExeGPT, a distributed system designed for
constraint-aware LLM inference. ExeGPT finds and runs with an optimal execution
schedule to maximize inference throughput while satisfying a given latency
constraint. By leveraging the distribution of input and output sequences, it
effectively allocates reso... | true | true | Hyungjun Oh and
Kihong Kim and
Jaemin Kim and
Sungkyun Kim and
Junyeol Lee and
Du{-}seong Chang and
Jiwon Seo | 2,024 | null | https://doi.org/10.1145/3620665.3640383 | 10.1145/3620665.3640383 | null | ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference | ASPLOS'24 - Lightning Talks - Session 2D - ExeGPT: Constraint ... | https://www.youtube.com/watch?v=UhBwDpY4hV4 | ... Inference Systems Paper Title: ExeGPT: Constraint-Aware Resource Scheduling for LLM Inference Authors: Hyungjun Oh, Kihong Kim, Jaemin Kim |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | Splitwise | \cite{Splitwise} | Splitwise: Efficient generative LLM inference using phase splitting | http://arxiv.org/abs/2311.18677v2 | Recent innovations in generative large language models (LLMs) have made their
applications and use-cases ubiquitous. This has led to large-scale deployments
of these models, using complex, expensive, and power-hungry AI accelerators,
most commonly GPUs. These developments make LLM inference efficiency an
important chal... | true | true | Pratyush Patel and
Esha Choukse and
Chaojie Zhang and
{\'{I}}{\~{n}}igo Goiri and
Aashaka Shah and
Saeed Maleki and
Ricardo Bianchini | 2,023 | null | https://doi.org/10.48550/arXiv.2311.18677 | 10.48550/ARXIV.2311.18677 | CoRR | Splitwise: Efficient generative LLM inference using phase splitting | Splitwise: Efficient generative LLM inference using phase splitting | http://arxiv.org/pdf/2311.18677v2 | Recent innovations in generative large language models (LLMs) have made their
applications and use-cases ubiquitous. This has led to large-scale deployments
of these models, using complex, expensive, and power-hungry AI accelerators,
most commonly GPUs. These developments make LLM inference efficiency an
important chal... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | PagedAttention | \cite{PagedAttention} | Efficient Memory Management for Large Language Model Serving with
PagedAttention | http://arxiv.org/abs/2309.06180v1 | High throughput serving of large language models (LLMs) requires batching
sufficiently many requests at a time. However, existing systems struggle
because the key-value cache (KV cache) memory for each request is huge and
grows and shrinks dynamically. When managed inefficiently, this memory can be
significantly wasted... | true | true | Woosuk Kwon and
Zhuohan Li and
Siyuan Zhuang and
Ying Sheng and
Lianmin Zheng and
Cody Hao Yu and
Joseph Gonzalez and
Hao Zhang and
Ion Stoica | 2,023 | null | https://doi.org/10.1145/3600006.3613165 | 10.1145/3600006.3613165 | null | Efficient Memory Management for Large Language Model Serving with
PagedAttention | Efficient Memory Management for Large Language Model ... | https://arxiv.org/pdf/2309.06180 | Efficient Memory Management for Large Language Model Serving with PagedAttention Woosuk Kwon 1,∗ Zhuohan Li 1,∗ Siyuan Zhuang 1 Ying Sheng 1,2 Lianmin Zheng 1 Cody Hao Yu 3 Joseph E. Gonzalez 1 Hao Zhang 4 Ion Stoica 1 1 UC Berkeley 2Stanford University 3Independent Researcher 4UC San Diego Abstract High throughput ser... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | Just-in-time | \cite{Just-in-time} | Just-in-time Quantization with Processing-In-Memory for Efficient ML
Training | http://arxiv.org/abs/2311.05034v1 | Data format innovations have been critical for machine learning (ML) scaling,
which in turn fuels ground-breaking ML capabilities. However, even in the
presence of low-precision formats, model weights are often stored in both
high-precision and low-precision during training. Furthermore, with emerging
directional data ... | true | true | Mohamed Assem Ibrahim and Shaizeen Aga and Ada Li and Suchita Pati and Mahzabeen Islam | 2,023 | null | https://arxiv.org/abs/2311.05034 | null | null | Just-in-time Quantization with Processing-In-Memory for Efficient ML
Training | Just-in-time Quantization with Processing-In-Memory for Efficient ML ... | https://arxiv.org/abs/2311.05034 | We explore just-in-time quantization (JIT-Q) where we only store high-precision weights in memory and generate low-precision weights only when needed. |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | llm_flash | \cite{llm_flash} | LLM in a flash: Efficient Large Language Model Inference with Limited
Memory | http://arxiv.org/abs/2312.11514v3 | Large language models (LLMs) are central to modern natural language
processing, delivering exceptional performance in various tasks. However, their
substantial computational and memory requirements present challenges,
especially for devices with limited DRAM capacity. This paper tackles the
challenge of efficiently run... | true | true | Keivan Alizadeh and
Iman Mirzadeh and
Dmitry Belenko and
Karen Khatamifard and
Minsik Cho and
Carlo C. Del Mundo and
Mohammad Rastegari and
Mehrdad Farajtabar | 2,023 | null | https://doi.org/10.48550/arXiv.2312.11514 | 10.48550/ARXIV.2312.11514 | CoRR | LLM in a flash: Efficient Large Language Model Inference with Limited
Memory | LLM in a Flash: Efficient Large Language Model Inference with ... | https://machinelearning.apple.com/research/efficient-large-language | # LLM in a Flash: Efficient Large Language Model Inference with Limited Memory This paper tackles the challenge of efficiently running LLMs that exceed the available DRAM capacity by storing the model parameters in flash memory, but bringing them on demand to DRAM. Our method involves constructing an inference cost mod... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | jawahar2023llm | \cite{jawahar2023llm} | LLM Performance Predictors are good initializers for Architecture Search | http://arxiv.org/abs/2310.16712v2 | In this work, we utilize Large Language Models (LLMs) for a novel use case:
constructing Performance Predictors (PP) that estimate the performance of
specific deep neural network architectures on downstream tasks. We create PP
prompts for LLMs, comprising (i) role descriptions, (ii) instructions for the
LLM, (iii) hype... | true | true | Jawahar, Ganesh and Abdul-Mageed, Muhammad and Lakshmanan, Laks VS and Ding, Dujian | 2,023 | null | null | null | arXiv preprint arXiv:2310.16712 | LLM Performance Predictors are good initializers for Architecture Search | LLM Performance Predictors are good initializers for Architecture Search | http://arxiv.org/pdf/2310.16712v2 | In this work, we utilize Large Language Models (LLMs) for a novel use case:
constructing Performance Predictors (PP) that estimate the performance of
specific deep neural network architectures on downstream tasks. We create PP
prompts for LLMs, comprising (i) role descriptions, (ii) instructions for the
LLM, (iii) hype... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | huang2024new | \cite{huang2024new} | New Solutions on LLM Acceleration, Optimization, and Application | http://arxiv.org/abs/2406.10903v1 | Large Language Models (LLMs) have become extremely potent instruments with
exceptional capacities for comprehending and producing human-like text in a
wide range of applications. However, the increasing size and complexity of LLMs
present significant challenges in both training and deployment, leading to
substantial co... | true | true | Huang, Yingbing and Wan, Lily Jiaxin and Ye, Hanchen and Jha, Manvi and Wang, Jinghua and Li, Yuhong and Zhang, Xiaofan and Chen, Deming | 2,024 | null | null | null | null | New Solutions on LLM Acceleration, Optimization, and Application | New Solutions on LLM Acceleration, Optimization, and Application | http://arxiv.org/pdf/2406.10903v1 | Large Language Models (LLMs) have become extremely potent instruments with
exceptional capacities for comprehending and producing human-like text in a
wide range of applications. However, the increasing size and complexity of LLMs
present significant challenges in both training and deployment, leading to
substantial co... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | liu2024optimizing | \cite{liu2024optimizing} | Optimizing LLM Queries in Relational Data Analytics Workloads | http://arxiv.org/abs/2403.05821v2 | Batch data analytics is a growing application for Large Language Models
(LLMs). LLMs enable users to perform a wide range of natural language tasks,
such as classification, entity extraction, and translation, over large
datasets. However, LLM inference is highly costly and slow: for example, an
NVIDIA L4 GPU running Ll... | true | true | Liu, Shu and Biswal, Asim and Cheng, Audrey and Mo, Xiangxi and Cao, Shiyi and Gonzalez, Joseph E and Stoica, Ion and Zaharia, Matei | 2,024 | null | null | null | arXiv preprint arXiv:2403.05821 | Optimizing LLM Queries in Relational Data Analytics Workloads | Optimizing LLM Queries in Relational Data Analytics Workloads | http://arxiv.org/pdf/2403.05821v2 | Batch data analytics is a growing application for Large Language Models
(LLMs). LLMs enable users to perform a wide range of natural language tasks,
such as classification, entity extraction, and translation, over large
datasets. However, LLM inference is highly costly and slow: for example, an
NVIDIA L4 GPU running Ll... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | hubara2018quantized | \cite{hubara2018quantized} | Quantized neural networks: Training neural networks with low precision weights and activations | null | null | true | false | Hubara, Itay and Courbariaux, Matthieu and Soudry, Daniel and El-Yaniv, Ran and Bengio, Yoshua | 2,018 | null | null | null | Journal of Machine Learning Research | Quantized neural networks: Training neural networks with low precision weights and activations | Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations | http://arxiv.org/pdf/1609.07061v1 | We introduce a method to train Quantized Neural Networks (QNNs) --- neural
networks with extremely low precision (e.g., 1-bit) weights and activations, at
run-time. At train-time the quantized weights and activations are used for
computing the parameter gradients. During the forward pass, QNNs drastically
reduce memory... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | GPT3.int8 | \cite{GPT3.int8} | LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale | null | null | true | false | Tim Dettmers and
Mike Lewis and
Younes Belkada and
Luke Zettlemoyer | 2,022 | null | http://papers.nips.cc/paper\_files/paper/2022/hash/c3ba4962c05c49636d4c6206a97e9c8a-Abstract-Conference.html | null | null | LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale | LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale | http://arxiv.org/pdf/2208.07339v2 | Large language models have been widely adopted but require significant GPU
memory for inference. We develop a procedure for Int8 matrix multiplication for
feed-forward and attention projection layers in transformers, which cut the
memory needed for inference by half while retaining full precision performance.
With our ... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | Deja | \cite{Deja} | Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time | http://arxiv.org/abs/2310.17157v1 | Large language models (LLMs) with hundreds of billions of parameters have
sparked a new wave of exciting AI applications. However, they are
computationally expensive at inference time. Sparsity is a natural approach to
reduce this cost, but existing methods either require costly retraining, have
to forgo LLM's in-conte... | true | true | Zichang Liu and
Jue Wang and
Tri Dao and
Tianyi Zhou and
Binhang Yuan and
Zhao Song and
Anshumali Shrivastava and
Ce Zhang and
Yuandong Tian and
Christopher R... | 2,023 | null | https://proceedings.mlr.press/v202/liu23am.html | null | null | Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time | Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time | http://arxiv.org/pdf/2310.17157v1 | Large language models (LLMs) with hundreds of billions of parameters have
sparked a new wave of exciting AI applications. However, they are
computationally expensive at inference time. Sparsity is a natural approach to
reduce this cost, but existing methods either require costly retraining, have
to forgo LLM's in-conte... |
CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the
Edge | 2506.02847v1 | SmoothQuant | \cite{SmoothQuant} | SmoothQuant: Accurate and Efficient Post-Training Quantization for Large
Language Models | http://arxiv.org/abs/2211.10438v7 | Large language models (LLMs) show excellent performance but are compute- and
memory-intensive. Quantization can reduce memory and accelerate inference.
However, existing methods cannot maintain accuracy and hardware efficiency at
the same time. We propose SmoothQuant, a training-free, accuracy-preserving,
and general-p... | true | true | Guangxuan Xiao and
Ji Lin and
Micka{\"{e}}l Seznec and
Hao Wu and
Julien Demouth and
Song Han | 2,023 | null | https://proceedings.mlr.press/v202/xiao23c.html | null | null | SmoothQuant: Accurate and Efficient Post-Training Quantization for Large
Language Models | SmoothQuant: Accurate and Efficient Post-Training Quantization for ... | https://arxiv.org/abs/2211.10438 | Image 2: arxiv logo>cs> arXiv:2211.10438 **arXiv:2211.10438** (cs) View a PDF of the paper titled SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models, by Guangxuan Xiao and 5 other authors View a PDF of the paper titled SmoothQuant: Accurate and Efficient Post-Training Quantizatio... |
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