المؤلفون: Chen, Yiming, Mahajan, Raghu, Tang, Haifeng

مصطلحات موضوعية: High Energy Physics - Theory

الوصف: Recently, S. Murthy has proposed a convergent expansion of free partition functions and superconformal indices of finite-$N$ purely adjoint gauge theories based on a Fredholm determinant expansion. This expansion has been dubbed the giant graviton expansion and takes the form of an infinite series of corrections to the $N=\infty$ result, with the $m^\text{th}$ correction being of order $e^{-mN}$. We show that this expansion can be reproduced using eigenvalue instantons in unitary matrix integrals. This perspective allows us to get the giant graviton expansion proposed by S. Murthy without the intermediate step of the Hubbard Stratonovich transformation.
Comment: 12 pages, 1 figure

الوصول الحر: http://arxiv.org/abs/2407.08155Test

المؤلفون: Sinha, Amit, Geist, Mathieu, Mahajan, Aditya

مصطلحات موضوعية: Computer Science - Machine Learning

الوصف: The standard approach for Partially Observable Markov Decision Processes (POMDPs) is to convert them to a fully observed belief-state MDP. However, the belief state depends on the system model and is therefore not viable in reinforcement learning (RL) settings. A widely used alternative is to use an agent state, which is a model-free, recursively updateable function of the observation history. Examples include frame stacking and recurrent neural networks. Since the agent state is model-free, it is used to adapt standard RL algorithms to POMDPs. However, standard RL algorithms like Q-learning learn a stationary policy. Our main thesis that we illustrate via examples is that because the agent state does not satisfy the Markov property, non-stationary agent-state based policies can outperform stationary ones. To leverage this feature, we propose PASQL (periodic agent-state based Q-learning), which is a variant of agent-state-based Q-learning that learns periodic policies. By combining ideas from periodic Markov chains and stochastic approximation, we rigorously establish that PASQL converges to a cyclic limit and characterize the approximation error of the converged periodic policy. Finally, we present a numerical experiment to highlight the salient features of PASQL and demonstrate the benefit of learning periodic policies over stationary policies.

الوصول الحر: http://arxiv.org/abs/2407.06121Test

المؤلفون: Gandhi, Sanket, Atul, Mahajan, Samanyu, Sharma, Vishal, Gupta, Rushil, Mondal, Arnab Kumar, Singla, Parag

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence

الوصف: Recent work has shown that object-centric representations can greatly help improve the accuracy of learning dynamics while also bringing interpretability. In this work, we take this idea one step further, ask the following question: "can learning disentangled representation further improve the accuracy of visual dynamics prediction in object-centric models?" While there has been some attempt to learn such disentangled representations for the case of static images \citep{nsb}, to the best of our knowledge, ours is the first work which tries to do this in a general setting for video, without making any specific assumptions about the kind of attributes that an object might have. The key building block of our architecture is the notion of a {\em block}, where several blocks together constitute an object. Each block is represented as a linear combination of a given number of learnable concept vectors, which is iteratively refined during the learning process. The blocks in our model are discovered in an unsupervised manner, by attending over object masks, in a style similar to discovery of slots \citep{slot_attention}, for learning a dense object-centric representation. We employ self-attention via transformers over the discovered blocks to predict the next state resulting in discovery of visual dynamics. We perform a series of experiments on several benchmark 2-D, and 3-D datasets demonstrating that our architecture (1) can discover semantically meaningful blocks (2) help improve accuracy of dynamics prediction compared to SOTA object-centric models (3) perform significantly better in OOD setting where the specific attribute combinations are not seen earlier during training. Our experiments highlight the importance discovery of disentangled representation for visual dynamics prediction.

الوصول الحر: http://arxiv.org/abs/2407.03216Test

المؤلفون: Mitra, Arindam, Del Corro, Luciano, Zheng, Guoqing, Mahajan, Shweti, Rouhana, Dany, Codas, Andres, Lu, Yadong, Chen, Wei-ge, Vrousgos, Olga, Rosset, Corby, Silva, Fillipe, Khanpour, Hamed, Lara, Yash, Awadallah, Ahmed

مصطلحات موضوعية: Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning

الوصف: Synthetic data is becoming increasingly important for accelerating the development of language models, both large and small. Despite several successful use cases, researchers also raised concerns around model collapse and drawbacks of imitating other models. This discrepancy can be attributed to the fact that synthetic data varies in quality and diversity. Effective use of synthetic data usually requires significant human effort in curating the data. We focus on using synthetic data for post-training, specifically creating data by powerful models to teach a new skill or behavior to another model, we refer to this setting as Generative Teaching. We introduce AgentInstruct, an extensible agentic framework for automatically creating large amounts of diverse and high-quality synthetic data. AgentInstruct can create both the prompts and responses, using only raw data sources like text documents and code files as seeds. We demonstrate the utility of AgentInstruct by creating a post training dataset of 25M pairs to teach language models different skills, such as text editing, creative writing, tool usage, coding, reading comprehension, etc. The dataset can be used for instruction tuning of any base model. We post-train Mistral-7b with the data. When comparing the resulting model Orca-3 to Mistral-7b-Instruct (which uses the same base model), we observe significant improvements across many benchmarks. For example, 40% improvement on AGIEval, 19% improvement on MMLU, 54% improvement on GSM8K, 38% improvement on BBH and 45% improvement on AlpacaEval. Additionally, it consistently outperforms other models such as LLAMA-8B-instruct and GPT-3.5-turbo.

الوصول الحر: http://arxiv.org/abs/2407.03502Test

المؤلفون: Nowara, Ewa M., Pinheiro, Pedro O., Mahajan, Sai Pooja, Mahmood, Omar, Watkins, Andrew Martin, Saremi, Saeed, Maser, Michael

مصطلحات موضوعية: Computer Science - Machine Learning, Quantitative Biology - Biomolecules

الوصف: We present NEBULA, the first latent 3D generative model for scalable generation of large molecular libraries around a seed compound of interest. Such libraries are crucial for scientific discovery, but it remains challenging to generate large numbers of high quality samples efficiently. 3D-voxel-based methods have recently shown great promise for generating high quality samples de novo from random noise (Pinheiro et al., 2023). However, sampling in 3D-voxel space is computationally expensive and use in library generation is prohibitively slow. Here, we instead perform neural empirical Bayes sampling (Saremi & Hyvarinen, 2019) in the learned latent space of a vector-quantized variational autoencoder. NEBULA generates large molecular libraries nearly an order of magnitude faster than existing methods without sacrificing sample quality. Moreover, NEBULA generalizes better to unseen drug-like molecules, as demonstrated on two public datasets and multiple recently released drugs. We expect the approach herein to be highly enabling for machine learning-based drug discovery. The code is available at https://github.com/prescient-design/nebulaTest

الوصول الحر: http://arxiv.org/abs/2407.03428Test

المؤلفون: Yadav, Vipin K., Vijaya, Y., Srikar, P. T., Prasad, B. Krishnam, Mahajan, Monika, Mallikarjun, K. V. L. N., Narendra, S., Adoni, Abhijit A., Rai, Vijay S., Veeresha, D. R., Zamani, Syeeda N.

مصطلحات موضوعية: Physics - Space Physics

الوصف: Aditya-L1 is the first Indian solar mission placed at the first Lagrangian (L1) point to study the Sun. A fluxgate magnetometer (MAG) is one of the seven payloads and one of the three in-situ payloads onboard to measure the interplanetary magnetic field (IMF) coming from the Sun towards the Earth. At present, the Aditya-L1 spacecraft is in a halo-orbit around the L1 point and the MAG payload is ON is continuously measuring the IMF. This paper presents the first measurements of the IMF by MAG.

الوصول الحر: http://arxiv.org/abs/2406.19757Test

المؤلفون: Asenjo, Felipe A., Mahajan, Swadesh M.

مصطلحات موضوعية: Astrophysics - High Energy Astrophysical Phenomena

الوصف: We show that dispersive gravitational waves, as a background spacetime, can reflect electromagnetic waves in a plasma. This reflection upshifts the frequency of the reflected wave, being larger for low-frequency incident waves. This effect takes place when the gravitational wave background propagates almost at the speed of light, allowing it to behave similar to a luminal mirror to electromagnetic plasma waves.

الوصول الحر: http://arxiv.org/abs/2406.18831Test

المؤلفون: Chakrabhavi, Vivek, Eniceicu, Dan Stefan, Mahajan, Raghu, Murdia, Chitraang

مصطلحات موضوعية: High Energy Physics - Theory

الوصف: We study ZZ instanton corrections in the $(2,4k)$ $N=1$ minimal superstring theory with the type 0B GSO projection, which becomes the type 0B $N=1$ super-JT gravity in the $k \to \infty$ limit. Each member of the $(2,4k)$ family of theories has two phases distinguished by the sign of the Liouville bulk cosmological constant. The worldsheet method for computing the one-loop normalization constant multiplying the instanton corrections gives an ill-defined answer in both phases. We fix these divergences using insights from string field theory and find finite, unambiguous results. Each member of the $(2,4k)$ family of theories is dual to a double-scaled one-matrix integral, where the double-scaling limit can be obtained starting either from a unitary matrix integral with a leading one-cut saddle point, or from a hermitian matrix integral with a leading two-cut saddle point. The matrix integral exhibits a gap-closing transition, which is the same as the double-scaled Gross-Witten-Wadia transition when $k=1$. We also compute instanton corrections in the double-scaled matrix integral for all $k$ and in both phases, and find perfect agreement with the string theory results.
Comment: 27 pages of main text + 27 pages of appendices + references

الوصول الحر: http://arxiv.org/abs/2406.16867Test

المؤلفون: Maheshwary, Rishabh, Yadav, Vikas, Nguyen, Hoang, Mahajan, Khyati, Madhusudhan, Sathwik Tejaswi

مصطلحات موضوعية: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning

الوصف: Instruction finetuning (IFT) is critical for aligning Large Language Models (LLMs) to follow instructions. While many effective IFT datasets have been introduced recently, they predominantly focus on high-resource languages like English. To better align LLMs across a broad spectrum of languages and tasks, we propose a fully synthetic, novel taxonomy (Evol) guided Multilingual, Multi-turn instruction finetuning dataset, called M2Lingual. It is constructed by first selecting a diverse set of seed examples and then utilizing the proposed Evol taxonomy to convert these seeds into complex and challenging multi-turn instructions. We demonstrate the effectiveness of M2Lingual by training LLMs of varying sizes and showcasing the enhanced performance across a diverse set of languages. We contribute the 2 step Evol taxonomy with the guided generation code: https://github.com/ServiceNow/M2LingualTest, as well as the first fully synthetic, general and task-oriented, multi-turn, multilingual dataset built with Evol - M2Lingual: https://huggingface.co/datasets/ServiceNow-AITest/ M2Lingual - containing 182K total IFT pairs, covering 70 languages and 17+ NLP tasks.
Comment: 39 pages

الوصول الحر: http://arxiv.org/abs/2406.16783Test

المؤلفون: Jiang, Tong, Baumgarten, Moritz K. A., Loos, Pierre-François, Mahajan, Ankit, Scemama, Anthony, Ung, Shu Fay, Zhang, Jinghong, Malone, Fionn D, Lee, Joonho

مصطلحات موضوعية: Physics - Chemical Physics

الوصف: ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [J. Chem. Theory Comput., 2022, 19(1): 109-121]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight the ease of incorporating different trial and walker types and the seamless integration of ipie with external libraries. We enable distributed Hamiltonian simulations, allowing for multi-GPU simulations of large systems. This development enabled us to compute the interaction energy of a benzene dimer with 84 electrons and 1512 orbitals, which otherwise would not have fit on a single GPU. We also support GPU-accelerated multi-slater determinant trial wavefunctions [arXiv:2406.08314] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu$_2$O$_2$]$^{2+}$ and [Fe$_2$S$_2$(SCH$_3$)]$^{2-}$. We also describe implementations of free projection AFQMC, finite temperature AFQMC, AFQMC for electron-phonon systems, and automatic differentiation in AFQMC for calculating physical properties. These advancements position ipie as a leading platform for AFQMC research in quantum chemistry, facilitating more complex and ambitious computational method development and their applications.
Comment: 17 pages, 13 figures

الوصول الحر: http://arxiv.org/abs/2406.16238Test