Researchers from Princeton University and Carnegie Mellon University have introduced the State Space Duality (SSD) framework, which connects state space models (SSMs) and attention mechanisms. This framework is highlighted in the new Mamba-2 model, a sequel to the original Mamba model introduced six months ago by Tri Dao and Albert Gu. Mamba-2 has been examined on recall abilities, in-context learning, and formal language expressivity. Additionally, the model aims to overcome quadratic bottlenecks in language modeling, providing more efficient algorithms and systems. The research community has shown significant interest in Mamba-2, recognizing its potential in improving language modeling efficiency. Other related advancements include Audio Mamba for audio representation learning and Brain-Mamba for encoding brain activity using selective state space models. Furthermore, the Chimera model effectively handles multivariate time series with 2-dimensional state space models.
Using two SSM heads with different discretization processes and input-dependent parameters, Chimera is provably able to learn long-term progression, seasonal patterns, and desirable dynamic autoregressive processes.
When it comes to #timeseries #forecasting, transformers aren't the answer. A new state space model architecture outperforms them all. 'Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models' by @behrouz_ali and coauthors. 🔥🔥🔥🔥🔥… https://t.co/EKJQTy4TUw
Are univariate SSMs effective when there are 2D dependencies? ✨Excited to share Chimera, our latest work on how to effectively model multivariate time series by input-dependent (selective) 2-dimensional state space models with fast training using a new 2D parallel scan. (1/8) https://t.co/Kk23rCeIoK
[AS] Audio Mamba: Bidirectional State Space Model for Audio Representation Learning https://t.co/PyIN9adZdt - Audio Mamba (AuM) is introduced, which is the first self-attention-free, purely SSM-based model for audio classification. It processes audio spectrograms using… https://t.co/V1RRdIZJHl
Read about "Brain-Mamba: Encoding Brain Activity via Selective State Space Models" by @behrouz_ali, @Farn8sh_h at CHIL2024!
Read our latest blog post on Mamba-2: Algorithms and Systems from authors @tri_dao and @_albertgu: https://t.co/XzPEvtjSOx https://t.co/7CDBYlm67Q
Unlocking Efficiency in Language Modeling: Mamba-2 and the State Space Duality Framework #AI #AItechnology #artificialintelligence #llm #machinelearning #Mamba2 https://t.co/oQimaqDZ8M https://t.co/m1E3ElC9ec
``Audio Mamba: Bidirectional State Space Model for Audio Representation Learning,'' Mehmet Hamza Erol, Arda Senocak, Jiu Feng, Joon Son Chung, https://t.co/WZSCP6iWFT
Beyond Quadratic Bottlenecks: Mamba-2 and the State Space Duality Framework for Efficient Language Modeling https://t.co/P0lOuUBlBc #LanguageModeling #Mamba2 #SSDframework #AIbusinesssolutions #AIsalesbot #ai #news #llm #ml #research #ainews #innovation #artificialintelligenc… https://t.co/8ZjgGcIfrb
Mamba got a sequel: Mamba-2 Six months ago, the Mamba research team Tri Dao and Albert Gu introduced their new model architecture. The community loved it. It has been examined on recall abilities, in-context learning, and formal language expressivity. So what's new? 🧵 https://t.co/9AhOfshdTF
Mamba-2 and State Space Models https://t.co/4BZ95eBBhb
Beyond Quadratic Bottlenecks: Mamba-2 and the State Space Duality Framework for Efficient Language Modeling Researchers from Princeton University and Carnegie Mellon University have introduced the State Space Duality (SSD) framework, which connects SSMs and attention mechanisms.… https://t.co/ccf5FRfxjz
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality https://t.co/pTSRkhRh2I
Mamba State-Space Models Can Be Strong Downstream Learners. https://t.co/pzXO7ZjMZd