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Found 22 papers, 5 papers with code
Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology.
Bridging the Gap between Chemical Reaction Pretraining and Conditional Molecule Generation with a Unified Model
Chemical reactions are the fundamental building blocks of drug design and organic chemistry research.
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models
This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or interactions between users in a social network.
Towards Reliable Item Sampling for Recommendation Evaluation
Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are "inconsistent" with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation.
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow
State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.
On the regularization landscape for the linear recommendation models
Recently, a wide range of recommendation algorithms inspired by deep learning techniques have emerged as the performance leaders several standard recommendation benchmarks.
On Extending NLP Techniques from the Categorical to the Latent Space: KL Divergence, Zipf's Law, and Similarity Search
Despite the recent successes of deep learning in natural language processing (NLP), there remains widespread usage of and demand for techniques that do not rely on machine learning.
Rosella: A Self-Driving Distributed Scheduler for Heterogeneous Clusters
We evaluate Rosella with a variety of workloads on a 32-node AWS cluster.
On Efficient Constructions of Checkpoints
Efficient construction of checkpoints/snapshots is a critical tool for training and diagnosing deep learning models.
BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation
In this work, we reexamine the inter-related problems of "topic identification" and "text segmentation" for sparse document learning, when there is a single new text of interest.
TF3P: Three-dimensional Force Fields Fingerprint Learned by Deep Capsular Network
Molecular fingerprints are the workhorse in ligand-based drug discovery.
Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing
Pricing assets has attracted significant attention from the financial technology community.
DeepScaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning
Furthermore, the generated compounds were evaluated by molecular docking in DRD2 targets and the results demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold and de novo drug design of potential drug candidates with specific docking scores.
Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle
In this paper, we define and investigate a general reward trans-formation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent's policy from original policy to a predefined target policy under MCE principle.
Adaptive Reduced Rank Regression
We study the low rank regression problem $\my = M\mx + \epsilon$, where $\mx$ and $\my$ are $d_1$ and $d_2$ dimensional vectors respectively.
Towards Non-Parametric Learning to Rank
Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix.
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design.
Ranked #4 on
Molecular Graph Generation
on InterBioScreen
From which world is your graph
This work focuses on unifying two of the most widely used link-formation models: the stochastic block model (SBM) and the small world (or latent space) model (SWM).
From which world is your graph?
Discovering statistical structure from links is a fundamental problem in the analysis of social networks.
Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization
We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day.
From Black-Scholes to Online Learning: Dynamic Hedging under Adversarial Environments
We consider a non-stochastic online learning approach to price financial options by modeling the market dynamic as a repeated game between the nature (adversary) and the investor.
Distributed Non-Stochastic Experts
This paper shows the difficulty of simultaneously achieving regret asymptotically better than \sqrt{kT} and communication better than T. We give a novel algorithm that for an oblivious adversary achieves a non-trivial trade-off: regret O(\sqrt{k^{5(1+\epsilon)/6} T}) and communication O(T/k^\epsilon), for any value of \epsilon in (0, 1/5).
