Data Machina #256
Data Machina Carlos
State Space Models (SSMs) are emerging as an alternative to Transformers for sequence modeling, with recent developments including Mamba-2 and applications in time-series forecasting, voice generation, and audio representation learning. Key advances include Chimera achieving superior performance on time-series benchmarks, Cartesian.ai's Sonic voice model using SSM inference for low latency, and Audio Mamba outperforming transformer baselines on self-supervised audio tasks. However, researchers at AllenAI and NYU argue that SSMs lack fundamental advantages over transformers for state tracking, suggesting their practical superiority may be limited despite recent optimism.
Why it matters
State Space Models (SSMs) An Alt to Transformers? Mamba-2. Chimera SSM Time-series. Audio Mamba. Sonic SSM Gen Voice. mamba.np. OSS Qwen-2 SOTA MLs. OSS LeRobot SOTA Robotics. Buffer of Thoughts.
